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IEEE Transactions on Services Computing
Journal Prestige (SJR): 0.87
Citation Impact (citeScore): 5
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-1374
Published by IEEE Homepage  [228 journals]
  • A Formal Treatment of Contract Signature

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      Authors: Ron van der Meyden;
      Pages: 3101 - 3114
      Abstract: The article develops a logical understanding of processes for signature of legal contracts, motivated by applications to legal recognition of smart contracts on blockchain platforms. A number of axioms and rules of inference are developed that can be used to justify a “meeting of the minds” precondition for contract formation from the fact that certain content has been signed. In addition to an “offer and acceptance” process, the article considers “signature in counterparts”, a legal process that permits a contract between two or more parties to be brought into force by having the parties independently (possibly, remotely) sign different copies of the contract, rather than placing their signatures on a common copy at a physical meeting. It is argued that a satisfactory account of signature in counterparts benefits from a logic with syntactic self-reference. The axioms used are supported by a formal semantics, and a number of further properties of the logic are investigated. In particular, it is shown that the logic implies that when a contract has been signed, the parties do not just agree, but are in mutual agreement (a common-knowledge-like notion) about the terms of the contract.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • A Missing QoS Prediction Approach via Time-Aware Collaborative Filtering

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      Authors: Endong Tong;Wenjia Niu;Jiqiang Liu;
      Pages: 3115 - 3128
      Abstract: Quality of Service (QoS) guarantee is an important issue in building service-oriented applications. Generally, some QoS values of a service are unknown to its users who have never invoked the service before. Fortunately, collaborative filtering (CF)-based methods are proved feasible for missing QoS prediction and have been widely used. However, these methods seldom took the temporal factors into consideration. Indeed, historical QoS values contain more information about user (or service) similarity. Furthermore, as the application environment is dynamic, obtained QoS values usually have short timeliness. Hence, using outdated QoSvalues will largely decrease the prediction accuracy. In order to resolve this issue, we proposed a time-aware collaborative filtering approach. First, we proposed a QoS model to filter out outdated QoS values, and divided the obtained QoSvalues into several time slices. Then, we computed the average value of historical QoS as temporal QoS forecast. In addition, by introducing time-aware similarity computation mechanism, we succeeded to select real similar neighbor users (or services) and further predict the CF-based QoS based on CF technology. Finally, we can predict the final missing QoS by combining temporal QoS forecast and CF-based QoS prediction. Experiment results show that our approach can receive better prediction precision.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Adaptive Data Dissemination Algorithm Based on Storing-Discarding
           Equilibrium for OUSNs

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      Authors: Linfeng Liu;Zhiyuan Xi;Jiagao Wu;Jia Xu;
      Pages: 3129 - 3142
      Abstract: Opportunistic underwater sensor networks (OUSNs) are deployed for various underwater applications, such as underwater creatures tracking and tactical surveillance. The data dissemination in OUSNs differs significantly from those in terrestrial wireless sensor networks or delay-tolerant networks, due to the signal irregularity in underwater communications and the limited storage capacity of the nodes in OUSNs. To alleviate the storage overflows on nodes and make room for the newly arriving data packets, some stored data packets ought to be actively discarded by nodes. This research begins with the construction of a differential equation set to describe the propagation process of data packets in OUSNs, and the storing-discarding equilibrium is investigated such that each data packet is expected to propagate and disappear during the allowable dissemination time slots. After that, the optimal storing probabilities and discarding probabilities are obtained for the nodes with different in-degrees to maximize the delivery ratio of data packets. Then, we propose an Adaptive Data Dissemination Algorithm (ADDA) for the storage-limited OUSNs with signal irregularity, where at each time slot the newly arriving data packets are stored and the stored data packets are discarded by nodes according to the obtained storing probabilities and discarding probabilities, respectively. Simulation results demonstrate the excellent performance of ADDA, showing that it can enhance the delivery ratio of data packets and reduce the number of storage overflows.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Blockchain Based Multi-Authority Fine-Grained Access Control System With
           Flexible Revocation

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      Authors: Meiyan Xiao;Qiong Huang;Ying Miao;Shunpeng Li;Willy Susilo;
      Pages: 3143 - 3155
      Abstract: Traditional data sharing systems are facing new challenges when implementing access control with more and more complex data sharing requirements. Flexibility of user revocation in completely decentralized environments needs to be taken into account. In this article, we propose a Key-Policy Attribute-Based Encryption scheme with Multiple and Flexible Revocation (MAFR-KP-ABE) to achieve the features of decentralized authorization and flexible revocation. We prove the security of our MAFR-KP-ABE scheme in the standard model and provide the comparison with relevant schemes to demonstrate its efficiency. Then we propose a fine-grained access control system based on MAFR-KP-ABE scheme and blockchain that matches the need of paid data sharing services with several security properties enhanced. Security analysis and system implementation are given subsequently to demonstrate our system efficient and secure.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Context-Enhanced Probabilistic Diffusion for Urban Point-of-Interest
           Recommendation

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      Authors: Zhipeng Zhang;Mianxiong Dong;Kaoru Ota;Yao Zhang;Yasuo Kudo;
      Pages: 3156 - 3169
      Abstract: Point-of-interest (POI) recommendation has a wide range of application values in smart city services computing. However, extreme sparsity of user-POI matrix seriously affects the recommendation accuracy. Rich contextual information is often utilized to solve data sparsity, whereas how to efficiently integrate them becomes another challenge. To this end, we merge the contextual information into probabilistic diffusion process to propose a novel approach, namely context-enhanced probabilistic diffusion, to generate satisfying POI recommendations under sparse data environment. First, the check-in data is preprocessed to construct the relevant scores that can reflect the relevant degrees between users and POIs expressly. Then, we extract social explicit and implicit trusts from user relationships, and integrate them with time influence to present a time-enhanced social diffusion process to obtain time-social probabilistic score. Next, by merging time factor into geographical distance, a time-enhanced geographical diffusion process is executed to generate time-geographical probabilistic score. Furthermore, we present a context-aware probabilistic matrix factorization to predict the relevant score for a target user on each POI. Finally, unchecked-in POIs with highest predicted relevant scores are recommended for the target user. Experiments executed on real-world datasets suggest that, the proposed approach outperforms the state-of-the-art approaches in terms of the recommendation accuracy.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • CSBR: A Compositional Semantics-Based Service Bundle Recommendation
           Approach for Mashup Development

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      Authors: Qi Gu;Jian Cao;Yancen Liu;
      Pages: 3170 - 3183
      Abstract: An increasing number of services are being offered which leads to difficulties in choosing appropriate services during mashup development. Currently, several service recommendation techniques have been developed for mashup creation, however, they are largely limited to suggesting services which have similar functionalities. The fundamental problem with these techniques is that they do not consider the large semantic gap between mashup descriptions and service descriptions. In this article, we propose a compositional semantics-based service bundle recommendation model (CSBR) to tackle this problem. CSBR is based on a semantic service package repository, which is constructed by mining the existing mashups. Specifically, the reusable service packages, which consist of multiple collaborative services, are annotated with composite semantics rather than their original semantics. Based on the semantic service package repository, CSBR can recommend a bundle of services that cover the functional requirements of the mashup as completely as possible. Extensive experiments are conducted on a real-world dataset and the results show CSBR achieves significant performance improvements in both precision and recall metrics over the state-of-the-art methods.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Defending Adversarial Attacks via Semantic Feature Manipulation

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      Authors: Shuo Wang;Surya Nepal;Carsten Rudolph;Marthie Grobler;Shangyu Chen;Tianle Chen;Zike An;
      Pages: 3184 - 3197
      Abstract: Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this article, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner. The intuition is that the classification result of a normal image is generally resistant to non-significant intrinsic feature changes, e.g., varying the thickness of handwritten digits. In contrast, adversarial examples are sensitive to such changes since the perturbation lacks transferability. To enable manipulation of features, a Combo-variational autoencoder is applied to learn disentangled latent codes that reveal semantic features. The resistance to classification change over the morphs, derived by varying and reconstructing latent codes, is used to detect suspicious inputs. Furthermore, Combo-VAE is enhanced to purify the adversarial examples with good quality by considering class-shared and class-unique features. We empirically demonstrate the effectiveness of detection and quality of purified instances. Our experiments on three datasets show that FM-Defense can detect nearly 100 percent of adversarial examples produced by different state-of-the-art adversarial attacks. It achieves more than 99 percent overall purification accuracy on the suspicious instances that close the manifold of clean examples.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Do Auto-Regressive Models Protect Privacy' Inferring Fine-Grained
           Energy Consumption From Aggregated Model Parameters

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      Authors: Nazim Uddin Sheikh;Hassan Jameel Asghar;Farhad Farokhi;Mohamed Ali Kaafar;
      Pages: 3198 - 3209
      Abstract: We investigate the extent to which statistical predictive models leak information about their training data. More specifically, based on the use case of household (electrical) energy consumption, we evaluate whether white-box access to auto-regressive (AR) models trained on such data together with background information, such as household energy data aggregates (e.g., monthly billing information) and publicly-available weather data, can lead to inferring fine-grained energy data of any particular household. We construct two adversarial models aiming to infer fine-grained energy consumption patterns. Both threat models use monthly billing information of target households. The second adversary has access to the AR model for a cluster of households containing the target household. Using two real-world energy datasets, we demonstrate that this adversary can apply maximum a posteriori estimation to reconstruct daily consumption of target households with significantly lower error than the first adversary, which serves as a baseline. Such fine-grained data can essentially expose private information, such as occupancy levels. Finally, we use differential privacy (DP) to alleviate the privacy concerns of the adversary in dis-aggregating energy data. Our evaluations show that differentially private model parameters offer strong privacy protection against the adversary with moderate utility, captured in terms of model fitness to the cluster.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • DTC: A Dynamic Transaction Chopping Technique for Geo-Replicated Storage
           Services

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      Authors: Ning Huang;Lihui Wu;Weigang Wu;Sajal K. Das;
      Pages: 3210 - 3223
      Abstract: Replicating data across geo-distributed datacenters is usually necessary for large scale cloud services to achieve high locality, durability and availability. One of the major challenges in such geo-replicated data services lies in consistency maintenance, which usually suffers from long latency due to costly coordination across datacenters. Among others, transaction chopping is an effective and efficient approach to address this challenge. However, existing chopping is conducted statically during programming, which is stubborn and complex for developers. In this article, we propose Dynamic Transaction Chopping (DTC), a novel technique that does transaction chopping and determines piecewise execution in a dynamic and automatic way. DTC mainly consists of two parts: a dynamic chopper to dynamically divide transactions into pieces according to the data partition scheme, and a conflict detection algorithm to check the safety of the dynamic chopping. Compared with existing techniques, DTC has several advantages: transparency to programmers, flexibility in conflict analysis, high degree of piecewise execution, and adaptability to data partition schemes. A prototype of DTC is implemented to verify the correctness of DTC and evaluate its performance. The experiment results show that our DTC technique can achieve much better performance than similar work.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Efficient Attribute Based Server-Aided Verification Signature

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      Authors: Yu Chen;Jiguo Li;Chengdong Liu;Jinguang Han;Yichen Zhang;Peng Yi;
      Pages: 3224 - 3232
      Abstract: Attribute based signature (ABS) is a novel cryptographic primitive, which permits users to sign a message over attributes without revealing other information. A signature only reveals that it is signed by a signer whose some attributes meet an access policy. However, some ABS schemes only support the threshold access policy, where the signing algorithms are limited by the threshold. The threshold access policy can not express precise access control well. In addition, the computation cost of the verification algorithm is heavy since pairing operations are required. Pairing is costly operation comparing to exponentiation. Therefore, existing ABS schemes are not suitable to resource-limited devices, such as RFID tags and smart cards. In order to solve the issues above, we present a novel ABS scheme by using the attribute tree as access policy that expresses flexible access control. We utilize server-aid technique to help the verifier to verify signatures and reduce the computation burden. Our scheme is proved secure against unforgeable and anonymous under chosen-policy selective-message attack in the standard model. Compared with existing schemes, our scheme is more efficient in terms of private key generation and verification. The proposed scheme reduces users’ calculation burden and expresses more flexible access policy.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Efficient Verification of Edge Data Integrity in Edge Computing
           Environment

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      Authors: Guangming Cui;Qiang He;Bo Li;Xiaoyu Xia;Feifei Chen;Hai Jin;Yang Xiang;Yun Yang;
      Pages: 3233 - 3244
      Abstract: The new edge computing paradigm extends cloud computing by allowing service vendors to deploy their service instances and data on distributed edge servers to serve their service users in close geographic proximity to those edge servers. Caching edge data on edge servers profoundly reduces the retrieval latency perceived by users. However, these edge data are subject to corruption due to intentional and/or accidental exceptions. This is a major challenge for service vendors but has been overlooked. Thus, verifying the integrity of edge data accurately and efficiently is a critical security problem in the edge computing environment. A unique characteristic of the edge computing environment is that edge servers suffer from constrained computing capacities. Thus, verifying data integrity on massive edge servers individually is computationally expensive and impractical. In this paper, we tackle this Edge Data Integrity (EDI) problem with an inspection and corruption localization scheme for EDI named ICL-EDI. This scheme allows service vendors to inspect data integrity and localize corrupted edge data cached on multiple edge servers accurately and efficiently. To evaluate its performance, we implement ICL-EDI and conduct extensive experiments to demonstrate its effectiveness and efficiency.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Faster Fog Computing Based Over-the-Air Vehicular Updates: A Transfer
           Learning Approach

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      Authors: Md. Al Maruf;Anil Singh;Akramul Azim;Nitin Auluck;
      Pages: 3245 - 3259
      Abstract: Fog computing is a promising option for time sensitive vehicular over-the-air (OTA) updates, as it can offer enhanced network durability and lower communication delays, as compared to the cloud. Fog node utilization for updates is non-deterministic, largely owing to the patterns in vehicular traffic. The resultant over provisioning of resources manifests itself in increased communication and handover delays. Based on an analysis of the regional traffic pattern for a particular time period, our proposed algorithm determines the optimal number of fog nodes required for OTA updates. In order to pinpoint the traffic load and perform fog node distribution, we employ k-means clustering. The efficacy of our proposed approach is demonstrated using a case study that considers handover delay, propagation delay, transmission rate and vehicular mobility to predict the OTA update time. We employ a machine learning model for predicting the communication delay between fog devices and vehicles. Using the European WiFi hotspot signal strength NYC dataset and the 5G dataset, we observe that the proposed approach increases the net reserve fog resources by 26.57 percent on an average, and reduces the OTA update time by 5.34 percent. We test the scalability of the proposed approach by analyzing the performance in terms of average throughput while varying the number of vehicles and OTA update size. We observe that a system with less traffic and small update size overall delivers a higher average throughput of 46 Mbps versus one with more traffic and large update size overall, which provides an average throughput of 30 Mbps. The performance of the proposed OTA update scheme on simulations has been corroborated by implementation on a real-world testbed.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Fine-Grained Elastic Partitioning for Distributed DNN Towards Mobile Web
           AR Services in the 5G Era

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      Authors: Pei Ren;Xiuquan Qiao;Yakun Huang;Ling Liu;Calton Pu;Schahram Dustdar;
      Pages: 3260 - 3274
      Abstract: Web-based Deep Neural Networks (DNNs) enhance the ability of object recognition and has attracted considerable attention in mobile Web AR and other services. However, neither performing the DNN inference on mobile Web browsers locally nor offloading computations to the cloud can strike a balance between accuracy and efficiency; generally, rude methods are often accompanied by unsatisfactory accuracy. Collaborative approaches seem to fill this gap by coordinating the distributed hierarchical computing resources, especially in the 5G era, but it still faces challenges in the current solutions, such as the lack of (1) full use of 5G resources for the one point DNN computation partitioning schemes; (2) fine-grained branching mechanism; (3) efficient partitioning method; and (4) multi-objective optimization. To this end, we present the fine-grained elastic computation partitioning mechanism for distributed DNN in 5G networks. First, we elaborate two collaborative scenarios. Second, we study the DNN branching mechanism at layer granularity. Next, we propose a DNN computation partitioning algorithm based on deep reinforcement learning. Finally, we develop a mobile Web AR application as a proof of concept. The experiments were conducted in an actually deployed 5G trial network, and the results show the superiority of this collaborative approach. The common theme is, under the premise that Quality of Service (QoS) is satisfied, to balance multiple interests by orchestrating computations across heterogeneous computing platforms.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • High Throughput Implementation of Post-Quantum Key Encapsulation and
           Decapsulation on GPU for Internet of Things Applications

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      Authors: Wai-Kong Lee;Seong Oun Hwang;
      Pages: 3275 - 3288
      Abstract: Internet of Things (IoT) sensor nodes are placed ubiquitously to collect information, which is then vulnerable to malicious attacks. For instance, adversaries can perform side channel attack on the sensor nodes to recover the symmetric key for encrypting IoT data. Refreshing the symmetric key frequently can reduce the risk of compromised keys. However, the number of sensor nodes connected to the gateway and cloud server is massive. Refreshed symmetric keys need to be sent to gateway devices and cloud server frequently with a secure key encapsulation mechanism (KEM), which is time-consuming. In this article, novel and efficient implementation techniques are proposed to accelerate Kyber, a post-quantum KEM, on a Graphics Processing Unit (GPU). Fully parallel implementation of number theoretic transform (NTT) with combined levels is presented, which is 2.65× faster than state-of-the-art result on a GPU. Other proposed techniques include parallel rejection sampling, central binomial distribution with coalesced memory access and parallel fine-grain AES-256. These techniques enable high throughput performance with 162760 encapsulations/second and 107631 decapsulations/second on an RTX2060 GPU. This is also the first fine grain implementation of post-quantum KEM (Kyber) on a GPU, which can be used to offer key encapsulation/decapsulation as a service to reduce the burden on IoT systems.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Incremental Entity Summarization With Formal Concept Analysis

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      Authors: Erhe Yang;Fei Hao;Yixuan Yang;Carmen De Maio;Aziz Nasridinov;Geyong Min;Laurence T. Yang;
      Pages: 3289 - 3303
      Abstract: Knowledge graph describes entities by numerous RDF data (subject-predicate-object triples), which has been widely applied in various fields, such as artificial intelligence, Semantic Web, entity summarization. With time elapses, the continuously increasing RDF descriptions of entity lead to information overload and further cause people confused. With this backdrop, automatic entity summarization has received much attention in recent years, aiming to select the most concise and most typical facts that depict an entity in brief from lengthy RDF data. As new descriptions of entity are continually coming, creating a compact summary of entity quickly from a lengthy knowledge graph is challenging. To address this problem, this article first formulates the problem and proposes a novel approach of Incremental Entity Summarization by leveraging Formal Concept Analysis (FCA), called IES-FCA. Additionally, we not only prove the rationality of our suggested method mathematically, but also carry out extensive experiments using two real-world datasets. The experimental results demonstrate that the proposed method IES-FCA can save about 8.7 percent of time consumption for all entities than the non-incremental entity summarization approach KAFCA at best. As for the effectiveness, IES-FCA outperforms the state-of-the-art algorithms in terms of $F1-measure$F1-measure, $MAP$MAP, and $NDCG$NDCG.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal
           Retrieval Service

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      Authors: Zhongwei Xie;Ling Liu;Yanzhao Wu;Lin Li;Luo Zhong;
      Pages: 3304 - 3316
      Abstract: It is widely acknowledged that learning joint embeddings of recipes with images is challenging due to the diverse composition and deformation of ingredients in cooking procedures. We present a Multi-modal Semantics enhanced Joint Embedding approach (MSJE) for learning a common feature space between the two modalities (text and image), with the ultimate goal of providing high-performance cross-modal retrieval services. Our MSJE approach has three unique features. First, we extract the TFIDF feature from the title, ingredients and cooking instructions of recipes. By determining the significance of word sequences through combining LSTM learned features with their TFIDF features, we encode a recipe into a TFIDF weighted vector for capturing significant key terms and how such key terms are used in the corresponding cooking instructions. Second, we combine the recipe TFIDF feature with the recipe sequence feature extracted through two-stage LSTM networks, which is effective in capturing the unique relationship between a recipe and its associated image(s). Third, we further incorporate TFIDF enhanced category semantics to improve the mapping of image modality and to regulate the similarity loss function during the iterative learning of cross-modal joint embedding. Experiments on the benchmark dataset Recipe1M show the proposed approach outperforms the state-of-the-art approaches.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • MapReduce Task Scheduling in Heterogeneous Geo-Distributed Data Centers

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      Authors: Xiaoping Li;Fuchao Chen;Rubén Ruiz;Jie Zhu;
      Pages: 3317 - 3329
      Abstract: Different data transmission times, processing times which are difficult to predict and node-dependent access times make MapReduce task scheduling rather complex. In this article, we consider the problem of scheduling MapReduce tasks to heterogeneous geo-distributed data centers to minimize the total tardiness. A new architecture is constructed to analyze data in the considered scenario. We model distinct data transmission levels, inter- and intra- data centers and heterogeneity of nodes mathematically. An algorithm framework is proposed to schedule MapReduce tasks to heterogeneous nodes in geographically distributed data centers. The proposed algorithm is suitable for both Hadoop MRv1 and MRv2. In terms of the number of idle containers detected in each heartbeat, the same number of tasks are selected from a sorted job sequence. For the map and reduce phases, two measurements are developed with data locality and completion time, respectively, based on which the classical Hungarian algorithm is adopted to optimally assign selected tasks to corresponding idle containers. Components and parameters of the proposal are statistically calibrated over a large set of random instances. A comparison of the proposed algorithm to existing methods for similar problems is carried out. Experimental results demonstrate the proposal is effective for the considered problem.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Mashup-Oriented Web API Recommendation via Multi-Model Fusion and
           Multi-Task Learning

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      Authors: Hao Wu;Yunhao Duan;Kun Yue;Lei Zhang;
      Pages: 3330 - 3343
      Abstract: As the number of Web APIs ever increases, choosing the appropriate APIs for mashup creations becomes more difficult. To tackle this problem, various methods have been proposed to recommend APIs to match requirements of mashups and achieved much success. However, there existed some challenges with feature fusion and utilization, textual requirement understanding, utilization of Mashup categories and compatibility evaluation. Therefore, we propose a neural framework (MTFM) based on multi-model fusion and multi-task learning for Mashup-oriented Web API recommendation. MTFM exploits a semantic component to generate representations of requirements and introduces a feature interaction component to model the feature interaction between mashups and Web APIs. Output features of both components are further fused to predict the candidate APIs, and this enables us to have both the advantages of content-based and collaborative filtering methods. We further introduce mashup category judgment as an auxiliary task, where both tasks are viewed as a multi-label learning problem and jointly optimized with multi-task learning. Also, we have extended MTFM to MTFM++ to take advantage of the metadata and quality features of APIs, and proposed a metric for compatibility evaluation. Experimental results on the ProgrammableWeb dataset show that our methods outperform most popular state-of-the-art methods.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Measuring Business Process Behavioral Similarity Based on Token Log
           Profile

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      Authors: Feifei Niu;Chuanyi Li;Jidong Ge;Lijie Wen;Zhongjin Li;Bin Luo;
      Pages: 3344 - 3357
      Abstract: Measuring business process similarity plays an important role in the analysis, management and optimization of business in big companies. In the early days, experts paid major attention to calculating business similarity according to corresponding process models. However, models only express ideal behavior of business processes without any undesired or unexpected business routines. In order to fully model business behavior, some researchers use system logs in similarity measuring. But previous system-logs-based similarity measurements have limitations on: (1) satisfaction of algorithm properties, (2) distribution of similarity values, and (3) complexity of algorithm. In this article, we take the advantages of token logs in process behavioral similarity measuring. Firstly, the Token Log Profile, modeled with a relation matrix, is defined as an abstraction of the initial token logs. Then, similarity between business processes is calculated based on their Token Log Profiles according to the proposed algorithm. Besides, we extend the properties that similarity algorithms should satisfy for evaluating the proposed algorithm. The experimental and analytical results show that our algorithm achieves very promising accuracy and efficiency while satisfying all the proposed properties compared with state-of-the-art algorithms.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Mobility-Aware IoT Application Placement in the Cloud – Edge
           Continuum

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      Authors: Dragi Kimovski;Narges Mehran;Christopher Emanuel Kerth;Radu Prodan;
      Pages: 3358 - 3371
      Abstract: The Edge computing extension of the Cloud services towards the network boundaries raises important placement challenges for IoT applications running in a heterogeneous environment with limited computing capacities. Unfortunately, existing works only partially address this challenge by optimizing a single or aggregate objective (e.g., response time), and not considering the edge devices’ mobility and resource constraints. To address this gap, we propose a novel mobility-aware multi-objective IoT application placement (mMAPO) method in the Cloud – Edge Continuum that optimizes completion time, energy consumption, and economic cost as conflicting objectives. mMAPO utilizes a Markov model for predictive analysis of the Edge device mobility and constrains the optimization to devices that do not frequently move through the network. We evaluate the quality of the mMAPO placements using simulation and real-world experimentation on two IoT applications. Compared to related work, mMAPO reduces the economic cost by 28 percent and decreases the completion time by 80 percent while maintaining a stable energy consumption.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Modeling and Empirical Validation of Reliability and Performance
           Trade-Offs of Dynamic Routing in Service- and Cloud-Based Architectures

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      Authors: Amirali Amiri;Uwe Zdun;André van Hoorn;
      Pages: 3372 - 3386
      Abstract: Context: Various patterns of dynamic routing architectures are used in service- and cloud-based environments, including sidecar-based routing, routing through a central entity such as an event store, or architectures with multiple dynamic routers. Objective: Choosing the wrong architecture may severely impact the reliability or performance of a software system. This article’s objective is to provide models and empirical evidence to precisely estimate the reliability and performance impacts. Method:We propose an analytical model of request loss for reliability modeling. We studied the accuracy of this model’s predictions empirically and calculated the error rate in 200 experiment runs, during which we measured the round-trip time performance and created a performance model based on multiple regression analysis. Finally, we systematically analyzed the reliability and performance impacts and trade-offs. Results and Conclusions:The comparison of the empirical data to the reliability model’s predictions shows a low enough and converging error rate for using the model during system architecting. The predictions of the performance model show that distributed approaches for dynamic data routing have a better performance compared to centralized solutions. Our results provide important new insights on dynamic routing architecture decisions to precisely estimate the trade-off between system reliability and performance.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Multiple Cooperative Task Allocation in Group-Oriented Social Mobile
           Crowdsensing

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      Authors: Wenan Tan;Lu Zhao;Bo Li;Lida Xu;Yun Yang;
      Pages: 3387 - 3401
      Abstract: Mobile crowdsensing, a new paradigm, has drawn much attention from the online community, in which mobile users are connected by using smartphones with sharing of information via mobile social networks. Multiple cooperative task allocation (MCTA) is a crucial problem in mobile crowdsensing, where each task requires more than one user to cooperatively complete. As more and more users join sensing tasks in groups, it is indispensable to develop a group-oriented crowdsensing mechanism supporting MCTA. However, existing studies generally focus on a group that can provide sufficient users to accomplish a task. Once these groups no longer exist, the corresponding task will be discarded or be performed with compromised quality. In this article, we propose a novel three-phase approach named Group-oriented Cooperative Crowdsensing (GoCC) to tackle the MCTA problem in social mobile crowdsensing. This approach exploits real-life relationships in the social network to form compatible groups, which improves the task coverage via group-oriented cooperation while achieving good task cooperation quality. Specifically, phase 1 selects a subset of users on the social network as initial leaders and directly pushes sensing tasks to them. Phase 2 utilizes the leaders to search for their socially connected users to model groups. Phase 3 presents the process of group-oriented task allocation for solving the MCTA problem. Experiments on the real-world dataset validate that our approach significantly outperforms the representative approaches.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Multiple Cooperative Task Assignment on Reliability-Oriented Social
           Crowdsourcing

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      Authors: Lu Zhao;Wenan Tan;Bo Li;Lida Xu;Yun Yang;
      Pages: 3402 - 3416
      Abstract: With the rapid development of mobile devices, mobile social networks have drawn increasing attention from spatial crowdsourcing in which users sharing information via social networking applications can easily identify and participate in multiple cooperative tasks. Existing studies generally assume that all users are trustworthy and can reliably perform assigned tasks. However, such assumptions do not hold in real-world practices. In this article, we consider an essential crowdsourcing problem, namely Reliability-oriented Socially-Aware Crowdsourcing (R-SAC), which improves the reliability by recruiting users who are better matched to the tasks. Our R-SAC problem is to recruit reliable users for multiple cooperative tasks so that the overall reliability of task assignment is maximized. We prove that the R-SAC problem is $mathcal {NP}$NP-hard. Then, we propose an approximation algorithm with a factor of $ln {m} + 1$lnm+1 to solve the R-SAC problem, where $m$m is the number of tasks. Specifically, user reliability refers to the probability that a user can reliably perform assigned tasks. To achieve reliable user recruitment during task assignment, we formulate the reliability of a user by combining the matching between the user and tasks, and the reliability feedback from neighbors who share similar be-aviors with the user in the social network. Besides, the distributed collaborative filtering technique is utilized to select the reliability feedback from the neighbors. We evaluate the performance of our proposed approach experimentally on two widely-used real-world datasets and the results demonstrate that our approach significantly outperforms five representative approaches.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Online-Offline Interactive Urban Crowd Flow Prediction Toward IoT-Based
           Smart City

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      Authors: Yuanyuan Zeng;Shujie Zhou;Kai Xiang;
      Pages: 3417 - 3428
      Abstract: Urban crowd flow prediction is very challenging for public management and planning in smart city applications. IoT based technologies make urban-scale flow detection and prediction possible. Existing work mostly focuses on spatial and temporal dependence based flow prediction by learning patterns from historical crowd flow data with prior knowledge such as weather, events and location attributes, etc. However, these approaches are not well suited for predictions of instantaneous flow change usually due to social emergency incidents and accidents, which are not with obvious patterns but vital for urban safety. In this article we propose an Online to Offline Interaction based Dilated Casual Convolutional Neural Network framework (O2O-DCNN) to make predictions on urban crowd flow. Both online attention behavior and offline crowd shift factors are considered in our framework, in case to capture the dependence between them and make more accurate predictions especially for instantaneous flow variations. The online and offline features are processed by dilated casual convolutions and then put into CBOW model based full connected network to make interactions. Our framework combines the causality of tempo-spatial related flow time series and semantic-based online attention behavior time series without too deep layers of neural network. The performance evaluations are based on realistic User Detail Record (UDR) dataset of a southern city in China provided by China Unicom. O2O-DCNN is compared with the other related baselines in terms of MASE and MAE. The results show that our framework is with much better accuracy, especially for instantaneous flow variation scenarios.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Pocket Diagnosis: Secure Federated Learning Against Poisoning Attack in
           the Cloud

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      Authors: Zhuoran Ma;Jianfeng Ma;Yinbin Miao;Ximeng Liu;Kim-Kwang Raymond Choo;Robert H. Deng;
      Pages: 3429 - 3442
      Abstract: Federated learning has become prevalent in medical diagnosis due to its effectiveness in training a federated model among multiple health institutions (i.e., Data Islands (DIs)). However, increasingly massive DI-level poisoning attacks have shed light on a vulnerability in federated learning, which inject poisoned data into certain DIs to corrupt the availability of the federated model. Previous works on federated learning have been inadequate in ensuring the privacy of DIs and the availability of the final federated model. In this article, we design a secure federated learning mechanism with multiple keys to prevent DI-level poisoning attacks for medical diagnosis, called SFAP. Concretely, SFAP provides privacy-preserving random forest-based federated learning by using the multi-key secure computation, which guarantees the confidentiality of DI-related information. Meanwhile, a secure defense strategy over encrypted locally-submitted models is proposed to resist DI-level poisoning attacks. Finally, our formal security analysis and empirical tests on a public cloud platform demonstrate the security and efficiency of SFAP as well as its capability of resisting DI-level poisoning attacks.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Privacy-Preserving Spatio-Temporal Keyword Search for Outsourced
           Location-Based Services

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      Authors: Qinlong Huang;Jiabao Du;Guanyu Yan;Yixian Yang;Qinglin Wei;
      Pages: 3443 - 3456
      Abstract: With the popularization of location-based services (LBS), encryption techniques have been utilized to protect data security when outsourcing LBS to cloud. However, existing schemes only consider spatial range search or keyword search, while expressive and practical search over encrypted LBS data is still a challenging problem. In this article, we introduce PrivSTL, a privacy-preserving spatio-temporal keyword search framework over the encrypted LBS data based on attribute-based encryption, linear encryption and RSA encryption. It allows mobile users to submit LBS query with spatial range, time interval and Boolean keyword expression, and provides accurate and authorized search by matching these query conditions and also the access policy. Then we introduce an extended scheme PrivSTG, which utilizes Geohash to divide the locations into grids, and outsources an encrypted index tree to cloud servers. PrivSTG improves the service efficiency by searching only over the ciphertexts in the surrounding grids of mobile user. Finally, we analyze the security of PrivSTL against chosen-plaintext, chosen-keyword and outside keyword-guessing attacks in generic bilinear group model, and show that PrivSTL guarantees the spatio-temporal keyword profile privacy, and also protects the query privacy. The experimental results indicate that our scheme is practical and efficient for outsourced LBS.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Quantitative Assessment of Service Pattern: Framework, Language, and
           Metrics

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      Authors: Meng Xi;Jianwei Yin;Jintao Chen;Ying Li;Shuiguang Deng;
      Pages: 3457 - 3470
      Abstract: For modern service industry (MSI), service pattern is a service provision approach to support the realisation of business model that involves participants from various domains and organizations. A comprehensive description and quantitative assessment of service patterns is of great significance for optimizing the organizational cooperation process in MSI and improving the competitiveness of enterprises. However, most relevant studies on service patterns stay at the level of business processes and qualitative analysis, lacking a comprehensive description of data, resources, and value exchanges among participants. Studies related to pattern assessment focus more on QoS (Quality of Service) rather than consideration of the utility of multi-participant collaboration. Hence, two issues need to be tackled for future development of MSI: a) How to systematically describe and distinguish service patterns with the same business processes. b) How to assess and compare service patterns quantitively and comprehensively. In this article, we propose a service pattern assessment framework which consists of two parts. As part one, we complement the service pattern description language (SPDL) with extended elements and observable attributes to empower it with quantitative analysis, namely Quantitative SPDL (SPDL-Q). In part two, a set of service pattern assessment metrics are designed to assess not only the quality of the services but also the cooperation efficiency of the participants and the orchestration effect of the service patterns elements. The proposed framework was then further validated by a case study, of which four E-commerce service patterns were studied to reveal their evolvement processes. Correlation experiments were also performed to identify the pattern features that have the greatest impact on each metric, so to provide guidance and suggestions for pattern design. Finally, the innovation and significance of the work are outlined and discussed.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Real-Time Detection and Mitigation of LDoS Attacks in the SDN Using the
           HGB-FP Algorithm

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      Authors: Dan Tang;Siqi Zhang;Yudong Yan;Jingwen Chen;Zheng Qin;
      Pages: 3471 - 3484
      Abstract: The software-defined network (SDN) has created the conditions for the optimization and development of network structures. However, its architecture is still not sufficient to resist or identify all denial of service (DoS) attacks, such as low-rate DoS (LDoS) attacks. Due to their low transporting rate and flash-crowd-like nature, LDoS attacks are well hidden in the background traffic and difficult to identify by anti-DoS mechanisms in the SDN. By implementing LDoS attacks in the SDN, we confirm that they can severely degrade the quality of service. We further propose a framework based on the histogram-based gradient boosting and finding peaks (HGB-FP) algorithm to detect LDoS attacks and mitigate their influence in the SDN in real-time. The histogram-based gradient boosting (HGB) algorithm, an ensemble learning with high quality and low complexity, can identify LDoS attacks quickly and accurately. The finding peaks (FP) algorithm locates the attacker via peak properties of the flow and installs flow rules on the switches to drop the attack flows. Experiments prove that our framework has higher accuracy and F-measure in identifying LDoS attacks than other machine learning approaches and mitigates the impact of LDoS attacks on bottleneck links in the SDN within seconds on average.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Reliable Policy Updating Under Efficient Policy Hidden Fine-Grained Access
           Control Framework for Cloud Data Sharing

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      Authors: Zuobin Ying;Wenjie Jiang;Ximeng Liu;Shengmin Xu;Robert H. Deng;
      Pages: 3485 - 3498
      Abstract: Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is one of the potent encryption paradigms in protecting data confidentiality in the cloud data sharing scenario. However, the access policy of the traditional CP-ABE is in plaintext form that reveals significant sensitive information of data owners and data visitors. To mitigate this problem, two approaches have been proposed in the literature. One is partially hidden, where the attributes in the access policy are divided into two parts: the plaintext attribute names and the hidden attribute values. The other approach fully hides the attributes in the access policy which, unfortunately, hinders efficient and correct decryption as well as dynamic policy-updating. In this article, we design a security-enhanced Attribute Cuckoo Filter (se-ACF) to hide the access policy and propose a new CP-ABE system, called Privacy-Preserving Policy Updating ABE (3PU-ABE), which effectively integrates policy hiding and policy updating. We conduct rigorous security analysis and performance evaluation of 3PU-ABE. The results indicate that 3PU-ABE completely hides the access policy without affecting the decryption, and entails better policy-updating efficiency than similar works.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Repeatable Multi-Dimensional Virtual Network Embedding in Cloud Service
           Platform

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      Authors: Weizhe Zhang;Desheng Wang;Shui Yu;Hui He;Yan Wang;
      Pages: 3499 - 3512
      Abstract: Virtual network embedding (VNE) can effectively deploy virtual networks (VNs) onto shared substrate network (SN) resources. However, with the consistent changing scalability and diversity demands of VNs, traditional VNE methods prove to be a challenging task for current cloud service platforms. Thus, we model a repeatable multi-dimensional virtual network embedding (RMD-VNE) problem for implementing multi-dimensional virtual networks (MD-VNs) that involves real servers, virtual machines, containers, and network simulators. The MD-VN is preprocessed and embedded via a heuristic method denoted as ReMiDvne. Following its transformation for the containers and simulation networks, the MD-VN topology undergoes a process of coarsening, partitioning, and uncoarsening. ReMiDvne then applies a topology-aware repeatable embedding solution to complete the embedding stage. Experimental results demonstrate that ReMiDvne outperforms seven baseline approaches through small-, 1,000- and 10,000-scale VNE simulation experiments. Remarkably, ReMiDvne improves the average rates of acceptance ratio, revenue, and revenue-cost ratio by up to 40.45, 40.45, and 299.03 percent, respectively, and reduces the average rate of cost by up to 64.16 percent. Furthermore, real-world VNE experiments are conducted based on the OpenStack platform. The results reveal the ability of ReMiDvne to efficiently reduce communication costs by up to 45.93 and 63.43 percent for download and upload, respectively.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Reputation Bootstrapping for Composite Services Using CP-Nets

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      Authors: Sajib Mistry;Athman Bouguettaya;
      Pages: 3513 - 3527
      Abstract: We propose a novel framework to bootstrap the reputation of on-demand service compositions. On-demand compositions are usually context-aware and have little or no direct consumer feedback. The reputation bootstrapping of single or atomic services do not consider the topology of the composition and relationships among reputation-related factors. We apply Conditional Preference Networks (CP-nets) of reputation-related factors for each of component services in a composition. The reputation of a composite service is bootstrapped by the composition of CP-nets. We consider the history of invocation among component services to determine reputation-interdependence in a composition. The composition rules are constructed using the composition topology and four types of reputation-influence among component services. A heuristic-based Q-learning approach is proposed to select the optimal set of reputation-related CP-nets. Experimental results prove the efficiency of the proposed approach.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • SilentDelivery: Practical Timed-Delivery of Private Information Using
           Smart Contracts

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      Authors: Chao Li;Balaji Palanisamy;
      Pages: 3528 - 3540
      Abstract: This article proposes SilentDelivery, a secure, scalable, and cost-efficient protocol for implementing timed information delivery service in a decentralized blockchain network. SilentDelivery employs a novel combination of threshold secret sharing and decentralized smart contracts. The protocol maintains shares of the decryption key of the private information of an information sender using a group of mailman recruited in a blockchain network before the specified future time-frame and restores the information to the information recipient at the required time-frame. To tackle the key challenges that limit the security and scalability of the protocol, SilentDelivery incorporates two novel countermeasure strategies. The first strategy, namely silent recruitment, enables a mailman to get recruited by a sender silently without the knowledge of any third party. The second strategy, namely dual-mode execution, makes the protocol run in a lightweight mode by default, where the cost of running smart contracts is significantly reduced. We rigorously analyze the security of SilentDelivery and implement the protocol over the Ethereum official test network. The results demonstrate that SilentDelivery is more secure and scalable compared to the state of the art and reduces the cost of running smart contracts by 85 percent.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Smooth Projective Hash Function From Codes and its Applications

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      Authors: Masoumeh Koochak Shooshtari;Mohammad Reza Aref;
      Pages: 3541 - 3553
      Abstract: Nowadays, Smooth Projective Hash Functions (SPHFs) play an important role in constructing cryptographic tools such as secure Password-based Authenticated Key Exchange (PAKE) protocol in the standard model, oblivious transfer, and zero-knowledge proofs. Specifically, in this article, we focus on constructing PAKE protocol; that is, a kind of key exchange protocol which needs only a low entropy password to produce a cryptographically strong shared session key. In spite of relatively good progress of SPHFs in applications, it seems there has been little effort to build them upon quantum-resistant assumptions such as lattice-based cryptography and code-based cryptography to make them secure against quantum computer attacks. More precisely, there are two proposals based on lattice assumptions that utilize the SPHFs to construct PAKE secured in standard model. Considering quantum-resistant assumptions is less than straightforward and needs some relaxations. In this article, we introduce two new Approximate SPHF (ASPHFs) from error-correcting codes. Upon designing ASPHF, we can construct two efficient PAKE protocols. The security of our protocols could be proved based on the hardness of bounded decoding (BD) problem and learning with parity (LPN) problem in the standard model.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • TNDP: Tensor-Based Network Distance Prediction With Confidence Intervals

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      Authors: Haojun Huang;Li Li;Geyong Min;Wang Miao;Yingying Zhu;Yangming Zhao;
      Pages: 3554 - 3565
      Abstract: The knowledge of network distances, in the form of delay or latency, for example, is beneficial to a number of distributed applications. Notice that it is difficult and expensive to implement global network measurements to obtain network distance, a feasible idea is to predict unknown distances by introducing network coordinates with limited network measurements. The existing solutions always represent the unknown network distances in a rather unique number. However, research and applications indicate that the real network distances are hard to be accurately figured out and changes subtly in an interval over time with the dynamic network environments. Accordingly, this article proposes a tensor-based network distance prediction (TNDP) approach to represent network distance with confidence intervals, by exploiting the random distance tensor and distributed matrix factorization. With a small set of network measurements among the nodes selected randomly, a distance matrix tensor has been established and factorized into the product of two location matrixes with the adaptive SGD-based learning solution. By introducing the important training determinants, including weight matrix, regularization coefficient, and minibatch gradient descent with the exponential decay rates, the unknown distances among nodes can be accurately inferred in the forms of confidence intervals, with quick convergence and less overfitting. Extensive experimental simulations on a wide variety of available data sets demonstrate that TNDP is superior to other approaches in terms of accuracy for network distance prediction.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Towards Multi-Client Forward Private Searchable Symmetric Encryption in
           Cloud Computing

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      Authors: Qingqing Gan;Xiaoming Wang;Daxin Huang;Jianwei Li;Dehua Zhou;Chao Wang;
      Pages: 3566 - 3576
      Abstract: As a useful cryptographic primitive, searchable symmetric encryption (SSE) has been intensively studied to achieve the secure and efficient retrieval of encrypted data. In order to process update operations, dynamic SSE schemes have been proposed. But recently, file-injection attack has threatened the security of traditional dynamic SSE protocols. Therefore, designing dynamic SSE schemes with forward privacy becomes a new demand to resist the above attack. Meanwhile, multi-client setting is another requirement in SSE techniques where multiple clients can be delegated and have access to the database. However, most of previous forward private schemes were constructed for single-client setting and cannot directly extended to multi-client environment efficiently. To solve the problem, we propose a forward private SSE scheme with support for multi-client in cloud computing. The proposed scheme is based on XOR-homomorphic function and involves two new data structures as private link and public search tree. Security proof demonstrates the proposed scheme can meet the desired secure features. We then conduct experimental evaluation of the proposed scheme and make comparison with related schemes. The result shows that the proposed scheme tends to have high efficiency.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • TrustWorker: A Trustworthy and Privacy-Preserving Worker Selection Scheme
           for Blockchain-Based Crowdsensing

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      Authors: Sheng Gao;Xiuhua Chen;Jianming Zhu;Xuewen Dong;Jianfeng Ma;
      Pages: 3577 - 3590
      Abstract: Worker selection in crowdsensing plays an important role in the quality control of sensing services. The majority of existing studies on worker selection were largely dependent on a trusted centralized server, which might suffer from single point of failure, the lack of transparency and so on. Some works recently proposed blockchain-based crowdsensing, which utilized reputation values stored on blockchains to select trusted workers. However, the transparency of blockchains enables attackers to effectively infer private information about workers by the disclosure of their reputation values. In this article, we proposed the TrustWorker, a trustworthy and privacy-preserving worker selection scheme for blockchain-based crowdsensing. By taking the advantages of blockchains such as decentralization, transparency and immutability, our TrustWorker could make the worker selection process trustworthy. To protect workers’ reputation privacy in our TrustWorker, we adopted a deterministic encryption algorithm to encrypt reputation values and then selected the top $N$N workers in the light of secret minimum heapsort scheme. Finally, we theoretically analyzed the effectiveness and efficiency of our TrustWorker, and then conducted a series of experiments. The theoretical analysis and experiment results demonstrate that our TrustWorker can achieve trustworthy worker selection, while ensuring the workers’ privacy and the high quality of sensing services.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Verifiable Semantic-Aware Ranked Keyword Search in Cloud-Assisted Edge
           Computing

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      Authors: Jiayi Li;Jianfeng Ma;Yinbin Miao;Lei Chen;Yunbo Wang;Ximeng Liu;Kim-Kwang Raymond Choo;
      Pages: 3591 - 3605
      Abstract: Ranked keyword search has gained Ranked keyword search has gained traction due to its attractive properties such as flexibility and accessibility. However, most existing ranked keyword search schemes ignore the semantic associations between the documents and queries. To solve this challenging issue in cloud-assisted edge computing, we first design the Semantic-aware Ranked Multi-keyword Search (SRMS) scheme by adopting the Latent Dirichlet Allocation (LDA) topic model and the Chinese Remainder Theorem (CRT)-based secret sharing mechanism. Considering that the cloud server may be malicious, we implement a basic verification mechanism in SRMS to verify the correctness and completeness of search results and extend this verification mechanism in cloud-assisted edge computing scenarios. Formal security analysis proves that SRMS and extended result verification mechanisms are secure in both the known ciphertext model and the known background model. Extensive experiments using the real-world dataset demonstrate that SRMS is efficient and practical.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • VFIRM: Verifiable Fine-Grained Encrypted Image Retrieval in Multi-Owner
           Multi-User Settings

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      Authors: Qiuyun Tong;Yinbin Miao;Lei Chen;Jian Weng;Ximeng Liu;Kim-Kwang Raymond Choo;Robert H. Deng;
      Pages: 3606 - 3619
      Abstract: To ensure the security of images outsourced to the malicious cloud without affecting searchability on such outsourced (typically encrypted) images, one could use privacy-preserving Content-Based Image Retrieval (CBIR) primitive. However, conventional privacy-preserving CBIR schemes based on Searchable Symmetric Encryption (SSE) are not capable of supporting efficient fine-grained access control and result verification simultaneously. Therefore, in this article, we propose a Verifiable Fine-grained encrypted Image Retrieval scheme in the Multi-owner multi-user settings (VFIRM). VFIRM first utilizes a novel polynomial-based access strategy to provide efficient fine-grained access control. Then, it employs the dual secure $k$k-nearest neighbor technique to distribute distinct keys to different data owners and data users, and finally implements an adapted homomorphic MAC technique to check the correctness of search results. Our formal security analysis shows that VFIRM is non-adaptive semantic secure if the client's search key is generated randomly and keeps in secret. Our empirical experiments using two real-world datasets (i.e., Caltech101 and Corel5k) demonstrate the practicality of VFIRM.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Web Service Network Embedding Based on Link Prediction and Convolutional
           Learning

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      Authors: Min Shi;Yuan Zhuang;Yufei Tang;Maohua Lin;Xingquan Zhu;Jianxun Liu;
      Pages: 3620 - 3633
      Abstract: Extensive efforts have been applied to develop efficient feature extraction algorithms, which aim to achieve optimal results in many fundamental tasks such as Web-based software service clustering, recommendation and composition. However, one common issue for existing methods is that mined features are problem dependent, causing poor generalization ability across different applications. Recent studies show that we can represent networked data (e.g., citation networks and social networks) as low-dimensional vectors with rich structure and content information preserved, which can then greatly facilitate many downstream tasks such as classification and clustering. In this article, we focus on the problem of Web service network embedding, which aims to learn low-dimensional vectors to represent services by encoding both Mashup-API composition structure and service functional content. We first propose a novel probabilistic topic model to predict potential links between Mashups and APIs in the service network. Then, we develop a Service Graph Convolutional Network (Service-GCN) to learn vector representations of services, where each service (e.g., Mashup or API) forms its representation through message passing between neighborhood services over the network. We evaluate the network embedding quality on two real-world datasets for downstream classification and clustering tasks. Experimental results show that the average performance of our method improves 20.7 percent (Micro-F1) in service classification and 19.0 percent (Accuracy) in Mashup clustering compared to the state-of-the-art, which verified the effectiveness of the proposed approach for learning vector representations of Web services.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • When Edge Caching Meets a Budget: Near Optimal Service Delivery in
           Multi-Tiered Edge Clouds

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      Authors: Qiufen Xia;Wenhao Ren;Zichuan Xu;Xin Wang;Weifa Liang;
      Pages: 3634 - 3648
      Abstract: More and more artificial intelligence (AI) applications, such as virtual reality (VR) and video analytics, are rapidly progressing towards enterprise and end-users with the promise of bringing immersive experience. Driven by the desire to improve users’ experience and promote business scenarios, such AI applications have unprecedented requirements for ultra-low latency as well as abundant computing resource in networks. Data centers in the core network can meet these demands by deploying various AI services and providing abundant resources. However, data transmission delay from data centers to end-users is too time-consuming because of traffic congestion in the core network, which compromises the performance of the AI applications. 5G and edge computing are emerging technologies to guarantee the timeliness for the delay-sensitive applications. The delay experienced by AI users can be significantly reduced, by ‘caching’ various services that are initially deployed at data centers to cloudlets in edge networks. Although ubiquitous edge service caching is always preferable for improving user experiences, it is impractical to cache all services from data centers to edge cloudlets, due to often limited caching budget of service providers and resource capacity constraints of cloudlets. Therefore, a service provider has to cautiously decide how many instances of a service can be cached, and where to cache the service instances. In this article, we investigate a fundamental problem of service caching from remote data centers to edge cloudlets in a multi-tiered edge cloud network. We first develop two approximation algorithms with approximation ratios to solve the problem for users demanding a single type of service. We then devise an efficient heuristic to solve the problem that users require different types of services. We finally conduct extensive experiments on a real test-bed to evaluate the perf-rmance of the proposed algorithms, and experimental results demonstrate that our algorithms can outperform some existing algorithms significantly.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Rethinking Fine-Grained Measurement From Software-Defined Perspective: A
           Survey

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      Authors: Hao Zheng;Yanan Jiang;Chen Tian;Long Cheng;Qun Huang;Weichao Li;Yi Wang;Qianyi Huang;Jiaqi Zheng;Rui Xia;Yi Wang;Wanchun Dou;Guihai Chen;
      Pages: 3649 - 3667
      Abstract: Network measurement provides operators an efficient tool for many network management tasks such as performance diagnosis, traffic engineering and intrusion prevention. However, with the rapid and continuous growth of traffic speed, it needs more computing and memory resources to monitor traffic in per-flow or per-packet granularity. Sample-based measurement systems (e.g., NetFlow, sFlow) have been developed to perform coarse-grained measurement, but they may miss part of records, especially for mice flows, which are important for some network management tasks (e.g., anomaly detection, performance diagnosis). To address these issues, data streaming algorithms such as hash tables and sketches have been introduced to balance the trade-off among accuracy, speed, and memory usage. In this article, we present a systematic survey of various data structures, algorithms and systems which have been proposed in recent years to perform fine-grained measurement for high-speed networks. We organize these methods and systems from a software-defined perspective. In particular, we abstract fine-grained network measurement into three-layer architecture. We introduce the responsibility of each layer and categorize existing state-of-the-art works into this architecture. Finally, we conclude the article and discuss the future directions of fine-grained network measurement.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • Security and Privacy for Healthcare Blockchains

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      Authors: Rui Zhang;Rui Xue;Ling Liu;
      Pages: 3668 - 3686
      Abstract: Healthcare blockchains provide an innovative way to store healthcare information, execute healthcare transactions, and build trust for healthcare data sharing and data integration in a decentralized open healthcare network environment. Although the healthcare blockchain technology has attracted broad interests and attention in industry, government and academia, the security and privacy concerns remain the focus of debate when deploying blockchains for information sharing in the healthcare sector from business operation to research collaboration. This article focuses on the security and privacy requirements for medical data sharing using blockchain, and provides a comprehensive analysis of the security and privacy risks and requirements, accompanied by technical solution techniques and strategies. First, we discuss the security and privacy requirements and attributes required for electronic medical data sharing by deploying the healthcare blockchain. Second, we categorize existing efforts into three reference blockchain usage scenarios for electronic medical data sharing, and discuss the technologies for implementing these security and privacy properties in the three categories of usage scenarios for healthcare blockchain, such as anonymous signatures, attribute-based encryption, zero-knowledge proofs, verification techniques for smart contract security. Finally, we discuss other potential blockchain application scenarios in healthcare sector. We conjecture that this survey will help healthcare professionals, decision makers, and healthcare service developers to gain technical and intuitive insights into the security and privacy of healthcare blockchains in terms of concepts, risks, requirements, development and deployment technologies and systems.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
  • 2022 Reviewers List*

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      Pages: 3687 - 3691
      Abstract: Presents a listing of reviewers who contributed to this publication in 2022.
      PubDate: Nov.-Dec. 1 2022
      Issue No: Vol. 15, No. 6 (2022)
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
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