Pub Date : 2024-08-02DOI: 10.1016/j.jpdc.2024.104966
Clustering is a pivotal primitive for unsupervised learning and data analysis. A popular variant is the -clustering problem, where, given a pointset P from a metric space, one must determine a subset S of k centers minimizing the sum of the ℓ-th powers of the distances of points in P from their closest centers. This formulation covers the well-studied k-median () and k-means () clustering problems. A more general variant, introduced to deal with noisy pointsets, features a further parameter z and allows up to z points of P (outliers) to be disregarded when computing the sum. We present a distributed coreset-based 3-round approximation algorithm for the -clustering problem with z outliers, using MapReduce as a computational model. An important feature of our algorithm is that it obliviously adapts to the intrinsic complexity of the dataset, captured by its doubling dimension D. Remarkably, for , our algorithm requires sublinear local memory per reducer, and yields a solution whose approximation ratio is an additive term away from the one achievable by the best known sequential (possibly bicriteria) algorithm, where γ can be made arbitrarily small. To the best of our knowledge, no previous distributed approaches were able to attain similar quality-performance tradeoffs for metrics with constant doubling dimension.
{"title":"MapReduce algorithms for robust center-based clustering in doubling metrics","authors":"","doi":"10.1016/j.jpdc.2024.104966","DOIUrl":"10.1016/j.jpdc.2024.104966","url":null,"abstract":"<div><p>Clustering is a pivotal primitive for unsupervised learning and data analysis. A popular variant is the <span><math><mo>(</mo><mi>k</mi><mo>,</mo><mi>ℓ</mi><mo>)</mo></math></span>-clustering problem, where, given a pointset <em>P</em> from a metric space, one must determine a subset <em>S</em> of <em>k</em> centers minimizing the sum of the <em>ℓ</em>-th powers of the distances of points in <em>P</em> from their closest centers. This formulation covers the well-studied <em>k</em>-median (<span><math><mi>ℓ</mi><mo>=</mo><mn>1</mn></math></span>) and <em>k</em>-means (<span><math><mi>ℓ</mi><mo>=</mo><mn>2</mn></math></span>) clustering problems. A more general variant, introduced to deal with noisy pointsets, features a further parameter <em>z</em> and allows up to <em>z</em> points of <em>P</em> (outliers) to be disregarded when computing the sum. We present a distributed coreset-based 3-round approximation algorithm for the <span><math><mo>(</mo><mi>k</mi><mo>,</mo><mi>ℓ</mi><mo>)</mo></math></span>-clustering problem with <em>z</em> outliers, using MapReduce as a computational model. An important feature of our algorithm is that it obliviously adapts to the intrinsic complexity of the dataset, captured by its doubling dimension <em>D</em>. Remarkably, for <span><math><mi>D</mi><mo>=</mo><mi>O</mi><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></math></span>, our algorithm requires sublinear local memory per reducer, and yields a solution whose approximation ratio is an additive term <span><math><mi>O</mi><mo>(</mo><mi>γ</mi><mo>)</mo></math></span> away from the one achievable by the best known sequential (possibly bicriteria) algorithm, where <em>γ</em> can be made arbitrarily small. To the best of our knowledge, no previous distributed approaches were able to attain similar quality-performance tradeoffs for metrics with constant doubling dimension.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0743731524001308/pdfft?md5=cb18e100c10527217dd5c5739d4b41d9&pid=1-s2.0-S0743731524001308-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939722","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}
Pub Date : 2024-07-23DOI: 10.1016/j.jpdc.2024.104955
Recently, accelerator-based compression/decompression was proposed to hide the storage latency of high-performance computing (HPC) applications that generate/ingest large data that cannot fit a node's memory. In this work, such a scheme has been implemented using a novel FPGA-based lossy compression/decompression scheme that has very low-latency. The proposed scheme completely overlaps the movement of the application's data with its compute kernels on the CPU with minimal impact on these kernels. Experiments showed that it can yield performance levels on-par with utilizing memory-only storage buffers, even though data is actually stored on disk. Experiments also showed that compared to CPU- and GPU-based compression frameworks, it achieves better performance levels at a fraction of the power consumption.
最近,有人提出了基于加速器的压缩/解压缩方案,以隐藏高性能计算(HPC)应用的存储延迟,这些应用会生成/测试无法容纳节点内存的大型数据。在这项工作中,这种方案采用了一种新颖的基于 FPGA 的有损压缩/解压缩方案,具有非常低的延迟。建议的方案将应用数据的移动与 CPU 上的计算内核完全重叠,对这些内核的影响最小。实验表明,尽管数据实际上存储在磁盘上,但该方案的性能水平与仅使用内存存储缓冲区的方案相当。实验还表明,与基于 CPU 和 GPU 的压缩框架相比,它能以极低的功耗实现更高的性能水平。
{"title":"Accelerating memory and I/O intensive HPC applications using hardware compression","authors":"","doi":"10.1016/j.jpdc.2024.104955","DOIUrl":"10.1016/j.jpdc.2024.104955","url":null,"abstract":"<div><p>Recently, accelerator-based compression/decompression was proposed to hide the storage latency of high-performance computing (HPC) applications that generate/ingest large data that cannot fit a node's memory. In this work, such a scheme has been implemented using a novel FPGA-based lossy compression/decompression scheme that has very low-latency. The proposed scheme completely overlaps the movement of the application's data with its compute kernels on the CPU with minimal impact on these kernels. Experiments showed that it can yield performance levels on-par with utilizing memory-only storage buffers, even though data is actually stored on disk. Experiments also showed that compared to CPU- and GPU-based compression frameworks, it achieves better performance levels at a fraction of the power consumption.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782289","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}
Pub Date : 2024-07-23DOI: 10.1016/j.jpdc.2024.104964
Background
Hospitals and medical facilities are increasingly concerned about network security and patient data privacy as the Internet of Medical Things (IoMT) infrastructures continue to develop. Researchers have studied customized network security frameworks and cyberattack detection tools driven by Artificial Intelligence (AI) to counter different types of attacks, such as spoofing, data alteration, and botnet attacks. However, carrying out routine IoMT services and tasks during an under-attack scenario is challenging. Machine Learning has been extensively suggested for detecting cyberattacks in IoMT and IoT infrastructures. However, the conventional centralized approach in ML cannot effectively detect newly emerging attacks without compromising patient data privacy and network flow data confidentiality.
Aim
This study discusses a Federated Bayesian Optimization XGBoost framework that employs multimodal sensory signals from patient vital signs and network flow data to detect attack patterns and malicious network traffic in IoMT infrastructure while ensuring data privacy and detecting previously unknown attacks.
Methodology
The proposed model employs a Federated Bayesian Optimisation XGBoost approach, which allows us to search the parameter space quickly and find an optimal solution from each local server while aggregating the model parameters from each local server to the centralised server. The XGBoost algorithm generates a new tree by taking into account the previously estimated value for the tree's input data and then optimizing the prediction gain. This study used a dataset with 44 attributes and 16 318 instances. During the preprocessing phase, 10 features were dropped, and the remaining 34 features were used to evaluate the network flows and biometric data (patient vital signs).
Results
The performance evaluation reveals that the proposed model predicts data alteration, malware, and spoofing attacks in patients' vital signs and network flow data with a prediction accuracy of 0.96. The results obtained from the experiment demonstrate that both the centralized and federated models are synchronized, with the latter occasionally being slightly reduced.
Conclusion
The findings indicate that the suggested model can be incorporated into the IoMT domain to detect malicious patterns while maintaining data privacy and confidentiality efficiently.
{"title":"Federated Bayesian optimization XGBoost model for cyberattack detection in internet of medical things","authors":"","doi":"10.1016/j.jpdc.2024.104964","DOIUrl":"10.1016/j.jpdc.2024.104964","url":null,"abstract":"<div><h3>Background</h3><p>Hospitals and medical facilities are increasingly concerned about network security and patient data privacy as the Internet of Medical Things (IoMT) infrastructures continue to develop. Researchers have studied customized network security frameworks and cyberattack detection tools driven by Artificial Intelligence (AI) to counter different types of attacks, such as spoofing, data alteration, and botnet attacks. However, carrying out routine IoMT services and tasks during an under-attack scenario is challenging. Machine Learning has been extensively suggested for detecting cyberattacks in IoMT and IoT infrastructures. However, the conventional centralized approach in ML cannot effectively detect newly emerging attacks without compromising patient data privacy and network flow data confidentiality.</p></div><div><h3>Aim</h3><p>This study discusses a Federated Bayesian Optimization XGBoost framework that employs multimodal sensory signals from patient vital signs and network flow data to detect attack patterns and malicious network traffic in IoMT infrastructure while ensuring data privacy and detecting previously unknown attacks.</p></div><div><h3>Methodology</h3><p>The proposed model employs a Federated Bayesian Optimisation XGBoost approach, which allows us to search the parameter space quickly and find an optimal solution from each local server while aggregating the model parameters from each local server to the centralised server. The XGBoost algorithm generates a new tree by taking into account the previously estimated value for the tree's input data and then optimizing the prediction gain. This study used a dataset with 44 attributes and 16 318 instances. During the preprocessing phase, 10 features were dropped, and the remaining 34 features were used to evaluate the network flows and biometric data (patient vital signs).</p></div><div><h3>Results</h3><p>The performance evaluation reveals that the proposed model predicts data alteration, malware, and spoofing attacks in patients' vital signs and network flow data with a prediction accuracy of 0.96. The results obtained from the experiment demonstrate that both the centralized and federated models are synchronized, with the latter occasionally being slightly reduced.</p></div><div><h3>Conclusion</h3><p>The findings indicate that the suggested model can be incorporated into the IoMT domain to detect malicious patterns while maintaining data privacy and confidentiality efficiently.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S074373152400128X/pdfft?md5=28ef82e7c7c3fa893ed6e8f14bc69244&pid=1-s2.0-S074373152400128X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785843","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}
Pub Date : 2024-07-23DOI: 10.1016/j.jpdc.2024.104965
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 M copies and sent to multi-anonymizers. Then, the random concurrent k-anonymization mechanism with multi-anonymizers is used to concurrently k-anonymize M 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.
{"title":"A trajectory privacy protection method using cached candidate result sets","authors":"","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":null,"pages":null},"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}
Pub Date : 2024-07-20DOI: 10.1016/S0743-7315(24)00124-2
{"title":"Front Matter 1 - Full Title Page (regular issues)/Special Issue Title page (special issues)","authors":"","doi":"10.1016/S0743-7315(24)00124-2","DOIUrl":"10.1016/S0743-7315(24)00124-2","url":null,"abstract":"","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0743731524001242/pdfft?md5=1e403c8d2d39fa3dcd92981eabc2fdd5&pid=1-s2.0-S0743731524001242-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141732164","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}
Pub Date : 2024-07-19DOI: 10.1016/j.jpdc.2024.104956
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.
{"title":"Verifiable and hybrid attribute-based proxy re-encryption for flexible data sharing in cloud storage","authors":"","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":null,"pages":null},"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}
Pub Date : 2024-07-14DOI: 10.1016/j.jpdc.2024.104954
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 G belongs to a given graph class . Such certifications with labels of size (where n 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.
In this work, we develop new decomposition tools for graph certification, and apply them to show that for every small enough minor H, H-minor-free graphs can indeed be certified with labels of size . We also show matching lower bounds using a new proof technique.
本地认证包括为网络顶点分配标签,以证明满足某些给定属性,这种方式可以在本地检查标签。最近几年,图类认证受到了广泛关注。这种认证的标签大小为 O(logn)(其中 n 是网络的大小),适用于树、平面图和嵌入曲面的图。在这项研究中,我们为图形认证开发了新的分解工具,并应用这些工具证明了对于每一个足够小的次要因子 H,无 H 次要因子的图形确实可以用大小为 O(logn) 的标签进行认证。我们还利用一种新的证明技术展示了匹配的下限。
{"title":"Local certification of graph decompositions and applications to minor-free classes","authors":"","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":null,"pages":null},"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}
Pub Date : 2024-07-09DOI: 10.1016/j.jpdc.2024.104953
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.
{"title":"Optimization-based disjoint and overlapping epsilon decompositions of large-scale dynamical systems via graph theory","authors":"","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":null,"pages":null},"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}
Pub Date : 2024-07-04DOI: 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
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-07-03DOI: 10.1016/j.jpdc.2024.104946
Khalid Javeed , Yasir Ali Shah , David Gregg
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 . 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 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 us 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.
{"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":null,"pages":null},"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}