Journal of Parallel and Distributed Computing最新文献_第8页

A trajectory privacy protection method using cached candidate result sets 使用缓存候选结果集的轨迹隐私保护方法

IF 3.4 3区计算机科学

Journal of Parallel and Distributed Computing Pub Date : 2024-07-23 DOI: 10.1016/j.jpdc.2024.104965

Zihao Shen , Yuyu Tang , Hui Wang , Peiqian Liu , Zhenqing Zheng

{"title":"A trajectory privacy protection method using cached candidate result sets","authors":"Zihao Shen , Yuyu Tang , Hui Wang , Peiqian Liu , Zhenqing Zheng","doi":"10.1016/j.jpdc.2024.104965","DOIUrl":"10.1016/j.jpdc.2024.104965","url":null,"abstract":"<div><p>A trajectory privacy protection method using cached candidate result sets (TPP-CCRS) is proposed for the user trajectory privacy leakage problem. First, the user's area is divided into a grid to lock the user's trajectory range, and a cache area is set on the user's mobile side to cache the candidate result sets queried from the user's area. Second, a security center is deployed to register users securely and assign public and private keys for verifying location information. The same user's location information is randomly divided into <em>M</em> copies and sent to multi-anonymizers. Then, the random concurrent <em>k</em>-anonymization mechanism with multi-anonymizers is used to concurrently <em>k</em>-anonymize <em>M</em> copies of location information. Finally, the prefix tree is added on the location-based service (LBS) server side, and the location information is encrypted using the clustered data fusion privacy protection algorithm. The optimal binary tree algorithm queries user interest points. Security analysis and experimental verification show that the TPP-CCRS can effectively protect user trajectory privacy and improve location information query efficiency.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"193 ","pages":"Article 104965"},"PeriodicalIF":3.4,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Front Matter 1 - Full Title Page (regular issues)/Special Issue Title page (special issues) 封面 1 - 完整扉页（常规期刊）/特刊扉页（特刊）

IF 3.4 3区计算机科学

Journal of Parallel and Distributed Computing Pub Date : 2024-07-20 DOI: 10.1016/S0743-7315(24)00124-2

引用次数: 0

Verifiable and hybrid attribute-based proxy re-encryption for flexible data sharing in cloud storage 基于属性的可验证混合代理重加密，实现云存储中的灵活数据共享

IF 3.4 3区计算机科学

Journal of Parallel and Distributed Computing Pub Date : 2024-07-19 DOI: 10.1016/j.jpdc.2024.104956

Lixue Sun , Chunxiang Xu , Fugeng Zeng

{"title":"Verifiable and hybrid attribute-based proxy re-encryption for flexible data sharing in cloud storage","authors":"Lixue Sun , Chunxiang Xu , Fugeng Zeng","doi":"10.1016/j.jpdc.2024.104956","DOIUrl":"10.1016/j.jpdc.2024.104956","url":null,"abstract":"<div><p>Cloud computing is a promising service architecture that enables a data owner to share data in an economic and efficient manner. To ensure data privacy, a data owner will generate the ciphertext of the data before outsourcing. Attribute-based encryption (ABE) provides an elegant solution for a data owner to enforce fine-grained access control on the data to be outsourced. However, ABE cannot support ciphertext transformation when needing to share the underlying data with a public-key infrastructure (PKI) user further. In addition, an untrusted cloud server may return random ciphertexts to the PKI user to save expensive computational costs of ciphertext transformation. To address above issues, we introduce a novel cryptographic primitive namely verifiable and hybrid attribute-based proxy re-encryption (VHABPRE). VHABPRE provides a transformation mechanism that re-encrypts an ABE ciphertext to a PKI-based public key encryption (PKE) ciphertext such that the PKI user can access the underlying data, meanwhile this PKI user can ensure the validity of the transformed ciphertext. By leveraging a key blinding technique and computing the commitment of the data, we construct two VHABPRE schemes to achieve flexible data sharing. We give formal security proofs and comprehensive performance evaluation to show the security and efficiency of the VHABPRE schemes.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"193 ","pages":"Article 104956"},"PeriodicalIF":3.4,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Local certification of graph decompositions and applications to minor-free classes 图分解的局部认证及其在无次要类中的应用

IF 3.4 3区计算机科学

Journal of Parallel and Distributed Computing Pub Date : 2024-07-14 DOI: 10.1016/j.jpdc.2024.104954

Nicolas Bousquet , Laurent Feuilloley , Théo Pierron

{"title":"Local certification of graph decompositions and applications to minor-free classes","authors":"Nicolas Bousquet , Laurent Feuilloley , Théo Pierron","doi":"10.1016/j.jpdc.2024.104954","DOIUrl":"10.1016/j.jpdc.2024.104954","url":null,"abstract":"<div><p>Local certification consists in assigning labels to the vertices of a network to certify that some given property is satisfied, in such a way that the labels can be checked locally. In the last few years, certification of graph classes received considerable attention. The goal is to certify that a graph <em>G</em> belongs to a given graph class <span><math><mi>G</mi></math></span>. Such certifications with labels of size <span><math><mi>O</mi><mo>(</mo><mi>log</mi><mo>⁡</mo><mi>n</mi><mo>)</mo></math></span> (where <em>n</em> is the size of the network) exist for trees, planar graphs and graphs embedded on surfaces. Feuilloley et al. ask if this can be extended to any class of graphs defined by a finite set of forbidden minors.</p><p>In this work, we develop new decomposition tools for graph certification, and apply them to show that for every small enough minor <em>H</em>, <em>H</em>-minor-free graphs can indeed be certified with labels of size <span><math><mi>O</mi><mo>(</mo><mi>log</mi><mo>⁡</mo><mi>n</mi><mo>)</mo></math></span>. We also show matching lower bounds using a new proof technique.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"193 ","pages":"Article 104954"},"PeriodicalIF":3.4,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141637436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization-based disjoint and overlapping epsilon decompositions of large-scale dynamical systems via graph theory 通过图论对大规模动力系统进行基于优化的不相交和重叠ε分解

IF 3.4 3区计算机科学

Journal of Parallel and Distributed Computing Pub Date : 2024-07-09 DOI: 10.1016/j.jpdc.2024.104953

Sahar Maleki, Hassan Zarabadipour, Mehdi Rahmani

{"title":"Optimization-based disjoint and overlapping epsilon decompositions of large-scale dynamical systems via graph theory","authors":"Sahar Maleki, Hassan Zarabadipour, Mehdi Rahmani","doi":"10.1016/j.jpdc.2024.104953","DOIUrl":"10.1016/j.jpdc.2024.104953","url":null,"abstract":"<div><p>To address the complexity challenge of a large-scale system, the decomposition into smaller subsystems is very crucial and demanding for distributed estimation and control purposes. This paper proposes novel optimization-based approaches to decompose a large-scale system into subsystems that are either weakly coupled or weakly coupled with overlapping components. To achieve this goal, first, the epsilon decomposition of large-scale systems is examined. Then, optimization frameworks are presented for disjoint and overlapping decompositions utilizing bipartite graphs. Next, the proposed decomposition algorithms are represented for particular cases of large-scale systems using directed graphs. In contrast to the existing user-based techniques, the proposed optimization-based methods can reach the solution rapidly and systematically. At last, the capability and efficiency of the proposed algorithms are investigated by conducting simulations on three case studies, which include a practical distillation column, a modified benchmark model, and the IEEE 118-bus power system.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"193 ","pages":"Article 104953"},"PeriodicalIF":3.4,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141623229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid 基于 LSTM 和自动编码器的异常检测，在智能电网中使用联合学习技术

IF 3.4 3区计算机科学

Journal of Parallel and Distributed Computing Pub Date : 2024-07-04 DOI: 10.1016/j.jpdc.2024.104951

Rakesh Shrestha , Mohammadreza Mohammadi , Sima Sinaei , Alberto Salcines , David Pampliega , Raul Clemente , Ana Lourdes Sanz , Ehsan Nowroozi , Anders Lindgren

{"title":"Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid","authors":"Rakesh Shrestha , Mohammadreza Mohammadi , Sima Sinaei , Alberto Salcines , David Pampliega , Raul Clemente , Ana Lourdes Sanz , Ehsan Nowroozi , Anders Lindgren","doi":"10.1016/j.jpdc.2024.104951","DOIUrl":"https://doi.org/10.1016/j.jpdc.2024.104951","url":null,"abstract":"<div><p>In smart electric grid systems, various sensors and Internet of Things (IoT) devices are used to collect electrical data at substations. In a traditional system, a multitude of energy-related data from substations needs to be migrated to central storage, such as Cloud or edge devices, for knowledge extraction that might impose severe data misuse, data manipulation, or privacy leakage. This motivates to propose anomaly detection system to detect threats and Federated Learning to resolve the issues of data silos and privacy of data. In this article, we present a framework to identify anomalies in industrial data that are gathered from the remote terminal devices deployed at the substations in the smart electric grid system. The anomaly detection system is based on Long Short-Term Memory (LSTM) and autoencoders that employs Mean Standard Deviation (MSD) and Median Absolute Deviation (MAD) approaches for detecting anomalies. We deploy Federated Learning (FL) to preserve the privacy of the data generated by the substations. FL enables energy providers to train shared AI models cooperatively without disclosing the data to the server. In order to further enhance the security and privacy properties of the proposed framework, we implemented homomorphic encryption based on the Paillier algorithm for preserving data privacy. The proposed security model performs better with MSD approach using HE-128 bit key providing 97% F1-score and 98% accuracy for K=5 with low computation overhead as compared with HE-256 bit key.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"193 ","pages":"Article 104951"},"PeriodicalIF":3.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0743731524001151/pdfft?md5=8b26b7d7db2b8eb9c771f42fd6536e0c&pid=1-s2.0-S0743731524001151-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141606835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GMC-crypto: Low latency implementation of ECC point multiplication for generic Montgomery curves over GF(p) GMC-crypto：针对 GF(p) 上通用蒙哥马利曲线的 ECC 点乘法的低延迟实现

IF 3.4 3区计算机科学

Journal of Parallel and Distributed Computing Pub Date : 2024-07-03 DOI: 10.1016/j.jpdc.2024.104946

Khalid Javeed , Yasir Ali Shah , David Gregg

{"title":"GMC-crypto: Low latency implementation of ECC point multiplication for generic Montgomery curves over GF(p)","authors":"Khalid Javeed , Yasir Ali Shah , David Gregg","doi":"10.1016/j.jpdc.2024.104946","DOIUrl":"https://doi.org/10.1016/j.jpdc.2024.104946","url":null,"abstract":"<div><p>Elliptic Curve Cryptography (ECC) is the front-runner among available public key cryptography (PKC) schemes due to its potential to offer higher security per key bit. All ECC-based cryptosystems heavily rely on point multiplication operation where its efficient realization has attained notable focus in the research community. Low latency implementation of the point multiplication operation is frequently required in high-speed applications such as online authentication and web server certification. This paper presents a low latency ECC point multiplication architecture for Montgomery curves over generic prime filed <span><math><mi>G</mi><mi>F</mi><mo>(</mo><mi>p</mi><mo>)</mo></math></span>. The proposed architecture is able to operate for a general prime modulus without any constraints on its structure. It is based on a new novel pipelined modular multiplier developed using the Montgomery multiplication and the Karatsuba-Offman technique with a four-part splitting methodology. The Montgomery ladder approach is adopted on a system level, where a high-speed scheduling strategy to efficiently execute <span><math><mi>G</mi><mi>F</mi><mo>(</mo><mi>p</mi><mo>)</mo></math></span> operations is also presented. Due to these circuit and system-level optimizations, the proposed design delivers low-latency results without a significant increase in resource consumption. The proposed architecture is described in Verilog-HDL for 256-bit key lengths and implemented on Virtex-7 and Virtex-6 FPGA platforms using Xilinx ISE Design Suite. On the Virtex-7 FPGA platform, it performs a 256-bit point multiplication operation in just 110.9 <em>u</em>s with a throughput of almost 9017 operations per second. The implementation results demonstrate that despite its generic nature, it produces low latency as compared to the state-of-the-art. Therefore, it has prominent prospects to be used in high-speed authentication and certification applications.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"193 ","pages":"Article 104946"},"PeriodicalIF":3.4,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neuron grouping and mapping methods for 2D-mesh NoC-based DNN accelerators 基于 2D 网格 NoC 的 DNN 加速器的神经元分组和映射方法

IF 3.4 3区计算机科学

Journal of Parallel and Distributed Computing Pub Date : 2024-07-02 DOI: 10.1016/j.jpdc.2024.104949

Furkan Nacar , Alperen Cakin , Selma Dilek , Suleyman Tosun , Krishnendu Chakrabarty

{"title":"Neuron grouping and mapping methods for 2D-mesh NoC-based DNN accelerators","authors":"Furkan Nacar , Alperen Cakin , Selma Dilek , Suleyman Tosun , Krishnendu Chakrabarty","doi":"10.1016/j.jpdc.2024.104949","DOIUrl":"https://doi.org/10.1016/j.jpdc.2024.104949","url":null,"abstract":"<div><p>Deep Neural Networks (DNNs) have gained widespread adoption in various fields; however, their computational cost is often prohibitively high due to the large number of layers and neurons communicating with each other. Furthermore, DNNs can consume a significant amount of energy due to the large volume of data movement and computation they require. To address these challenges, there is a need for new architectures to accelerate DNNs. In this paper, we propose novel neuron grouping and mapping methods for 2D-mesh Network-on-Chip (NoC)-based DNN accelerators considering both fully connected and partially connected DNN models. We present Integer Linear Programming (ILP) and simulated annealing (SA)-based neuron grouping solutions with the objective of minimizing the total volume of data communication among the neuron groups. After determining a suitable graph representation of the DNN, we also apply ILP and SA methods to map the neurons onto a 2D-mesh NoC fabric with the objective of minimizing the total communication cost of the system. We conducted several experiments on various benchmarks and DNN models with different pruning ratios and achieved an average of 40-50% improvement in communication cost.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"193 ","pages":"Article 104949"},"PeriodicalIF":3.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliable communication in dynamic networks with locally bounded byzantine faults 具有局部有界拜占庭故障的动态网络中的可靠通信

IF 3.4 3区计算机科学

Journal of Parallel and Distributed Computing Pub Date : 2024-07-02 DOI: 10.1016/j.jpdc.2024.104952

Silvia Bonomi , Giovanni Farina , Sébastien Tixeuil

引用次数: 0

PiPar: Pipeline parallelism for collaborative machine learning PiPar：协作式机器学习的管道并行性

IF 3.4 3区计算机科学

Journal of Parallel and Distributed Computing Pub Date : 2024-07-02 DOI: 10.1016/j.jpdc.2024.104947

Zihan Zhang , Philip Rodgers , Peter Kilpatrick , Ivor Spence , Blesson Varghese

{"title":"PiPar: Pipeline parallelism for collaborative machine learning","authors":"Zihan Zhang , Philip Rodgers , Peter Kilpatrick , Ivor Spence , Blesson Varghese","doi":"10.1016/j.jpdc.2024.104947","DOIUrl":"https://doi.org/10.1016/j.jpdc.2024.104947","url":null,"abstract":"<div><p>Collaborative machine learning (CML) techniques, such as federated learning, have been proposed to train deep learning models across multiple mobile devices and a server. CML techniques are privacy-preserving as a local model that is trained on each device instead of the raw data from the device is shared with the server. However, CML training is inefficient due to low resource utilization. We identify idling resources on the server and devices due to sequential computation and communication as the principal cause of low resource utilization. A novel framework <span>PiPar</span> that leverages pipeline parallelism for CML techniques is developed to substantially improve resource utilization. A new training pipeline is designed to parallelize the computations on different hardware resources and communication on different bandwidth resources, thereby accelerating the training process in CML. A low overhead automated parameter selection method is proposed to optimize the pipeline, maximizing the utilization of available resources. The experimental results confirm the validity of the underlying approach of <span>PiPar</span> and highlight that when compared to federated learning: (i) the idle time of the server can be reduced by up to 64.1×, and (ii) the overall training time can be accelerated by up to 34.6× under varying network conditions for a collection of six small and large popular deep neural networks and four datasets without sacrificing accuracy. It is also experimentally demonstrated that <span>PiPar</span> achieves performance benefits when incorporating differential privacy methods and operating in environments with heterogeneous devices and changing bandwidths.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"193 ","pages":"Article 104947"},"PeriodicalIF":3.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0743731524001114/pdfft?md5=589f02b2eaa1e2c9523c4d2a0434e4e1&pid=1-s2.0-S0743731524001114-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0