SECURITY AND PRIVACY最新文献_第2页

A systematic survey on security and privacy issues of medicine supply chain: Taxonomy, framework, and research challenges 关于医药供应链安全和隐私问题的系统调查：分类、框架和研究挑战

SECURITY AND PRIVACY Pub Date : 2024-02-07 DOI: 10.1002/spy2.377

Jigna J. Hathaliya, S. Tanwar

{"title":"A systematic survey on security and privacy issues of medicine supply chain: Taxonomy, framework, and research challenges","authors":"Jigna J. Hathaliya, S. Tanwar","doi":"10.1002/spy2.377","DOIUrl":"https://doi.org/10.1002/spy2.377","url":null,"abstract":"Several decades ago, the medicine supply chain (MSC) transferred the medicines from the manufacturer to the end‐consumer and kept all records in a manual register. The manual intermediary management of MSC and medicine data often leads to issues like unauthorized third parties participating in the process and illegally tempering medicine data. As a result of this medicine temperament, end users get counterfeit medicine that poses severe consequences for patients' health. Over time, manual data management and intermediaries transform into digital platforms that track, manage, and exchange data in real‐time. Real‐time data exchange opens attackers up to target MSCs, access medicine data illegally, and modify medicine conditions, locations, and specifications. With the objective of this, the proposed survey identifies security and privacy issues and discusses security solutions. This security solution involves various data security and privacy frameworks such as micro‐segmentation, zero trust model, and many other software‐based security solutions. Moreover, The proposed survey explores radio frequency identification for medicine tracking in which each intermediary transforms the medicine location over the internet. In contrast, the Internet of Things is used to exchange medicine temperature conditions in real‐time. Furthermore, cybersecurity‐based solutions help protect against malicious threats, and blockchain keeps data private and temper‐proof. Artificial intelligence provides machine learning and deep learning models for analyzing large amounts of data to generate insights from the MSC data. Therefore, this survey addresses the various security and privacy issues and provides solutions that help researchers carry out work in this domain.","PeriodicalId":506233,"journal":{"name":"SECURITY AND PRIVACY","volume":"52 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139855760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy preserving unique identity generation from multimodal biometric data for privacy and security applications 从多模态生物识别数据中生成保护隐私的唯一身份，用于隐私和安全应用

SECURITY AND PRIVACY Pub Date : 2024-02-07 DOI: 10.1002/spy2.375

Priyabrata Dash, M. Sarma, Debasis Samanta

引用次数: 0

Algorithm to satisfy l‐diversity by combining dummy records and grouping 通过合并虚假记录和分组满足 l 多样性的算法

SECURITY AND PRIVACY Pub Date : 2024-02-07 DOI: 10.1002/spy2.373

Keiichiro Oishi, Y. Sei, J. Andrew, Yasuyuki Tahara, Akihiko Ohsuga

{"title":"Algorithm to satisfy l‐diversity by combining dummy records and grouping","authors":"Keiichiro Oishi, Y. Sei, J. Andrew, Yasuyuki Tahara, Akihiko Ohsuga","doi":"10.1002/spy2.373","DOIUrl":"https://doi.org/10.1002/spy2.373","url":null,"abstract":"Universities and corporations frequently use personal information databases for diverse objectives, such as research and marketing. The use of these databases inherently intersects with privacy issues, which have been the subject of extensive research. Traditional anonymization techniques predominantly focus on removing or altering identifiers and quasi‐identifiers (QIDs), the latter of which, although not unique, are closely correlated with individuals. However, this modification of QIDs can often impede data analysis. In this study, we introduce an innovative anonymization algorithm that combines the dummy‐record addition technique with a grouping method while circumventing the modification of QIDs. This fusion reduces the number of dummy records required for effective anonymization. The principal contribution of this study is the algorithm's ability to reduce the number of added dummy records. The proposed algorithm not only retains a high degree of data usefulness but also successfully adheres to the ‐diversity standard, which is a critical metric in privacy security. The experimental findings demonstrate that the proposed method offers a more equitable balance between safety and utility than existing technological solutions.","PeriodicalId":506233,"journal":{"name":"SECURITY AND PRIVACY","volume":"63 5-6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139856822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Algorithm to satisfy l‐diversity by combining dummy records and grouping 通过合并虚假记录和分组满足 l 多样性的算法

SECURITY AND PRIVACY Pub Date : 2024-02-07 DOI: 10.1002/spy2.373

Keiichiro Oishi, Y. Sei, J. Andrew, Yasuyuki Tahara, Akihiko Ohsuga

{"title":"Algorithm to satisfy l‐diversity by combining dummy records and grouping","authors":"Keiichiro Oishi, Y. Sei, J. Andrew, Yasuyuki Tahara, Akihiko Ohsuga","doi":"10.1002/spy2.373","DOIUrl":"https://doi.org/10.1002/spy2.373","url":null,"abstract":"Universities and corporations frequently use personal information databases for diverse objectives, such as research and marketing. The use of these databases inherently intersects with privacy issues, which have been the subject of extensive research. Traditional anonymization techniques predominantly focus on removing or altering identifiers and quasi‐identifiers (QIDs), the latter of which, although not unique, are closely correlated with individuals. However, this modification of QIDs can often impede data analysis. In this study, we introduce an innovative anonymization algorithm that combines the dummy‐record addition technique with a grouping method while circumventing the modification of QIDs. This fusion reduces the number of dummy records required for effective anonymization. The principal contribution of this study is the algorithm's ability to reduce the number of added dummy records. The proposed algorithm not only retains a high degree of data usefulness but also successfully adheres to the ‐diversity standard, which is a critical metric in privacy security. The experimental findings demonstrate that the proposed method offers a more equitable balance between safety and utility than existing technological solutions.","PeriodicalId":506233,"journal":{"name":"SECURITY AND PRIVACY","volume":"38 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139797035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using smart grid infrastructure for authentication and data management in Internet of Medical Things 在医疗物联网中使用智能电网基础设施进行身份验证和数据管理

SECURITY AND PRIVACY Pub Date : 2024-02-06 DOI: 10.1002/spy2.376

Ikram Ud Din, Wajahat Ali, Ahmad S. Almogren, Iehab Alrassan, S. Zeadally

{"title":"Using smart grid infrastructure for authentication and data management in Internet of Medical Things","authors":"Ikram Ud Din, Wajahat Ali, Ahmad S. Almogren, Iehab Alrassan, S. Zeadally","doi":"10.1002/spy2.376","DOIUrl":"https://doi.org/10.1002/spy2.376","url":null,"abstract":"The Internet of Medical Things (IoMT) has recently become the norm in medical operations and emergency situations. A physician or a medical personnel is interested in accessing the patients' data from remote locations using IoMT. The patients' medical management system should allow their doctors and family members to have access to the data, especially in emergency situations. Deploying sensor nodes and managing the large amount of medical data generated by these sensors require a system that provides protection against known security threats and a robust mechanism to recover when subjected to attacks. The proposed scheme that provides mutual authentication between user(s) and power generation nodes, and also mitigate against attacks by validating the identity during the authentication phase. Moreover, the proposed scheme, called Grid‐Based Authentication, uses the existing smart grid infrastructure for communications, which is a model of integrated infrastructure in a smart city. Furthermore, the simulation results show that our proposed scheme yields better performance in terms of average throughput, end to end delay, and computation and communication costs compared with other state‐of‐the‐art approaches.","PeriodicalId":506233,"journal":{"name":"SECURITY AND PRIVACY","volume":"75 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139861080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using smart grid infrastructure for authentication and data management in Internet of Medical Things 在医疗物联网中使用智能电网基础设施进行身份验证和数据管理

SECURITY AND PRIVACY Pub Date : 2024-02-06 DOI: 10.1002/spy2.376

Ikram Ud Din, Wajahat Ali, Ahmad S. Almogren, Iehab Alrassan, S. Zeadally

{"title":"Using smart grid infrastructure for authentication and data management in Internet of Medical Things","authors":"Ikram Ud Din, Wajahat Ali, Ahmad S. Almogren, Iehab Alrassan, S. Zeadally","doi":"10.1002/spy2.376","DOIUrl":"https://doi.org/10.1002/spy2.376","url":null,"abstract":"The Internet of Medical Things (IoMT) has recently become the norm in medical operations and emergency situations. A physician or a medical personnel is interested in accessing the patients' data from remote locations using IoMT. The patients' medical management system should allow their doctors and family members to have access to the data, especially in emergency situations. Deploying sensor nodes and managing the large amount of medical data generated by these sensors require a system that provides protection against known security threats and a robust mechanism to recover when subjected to attacks. The proposed scheme that provides mutual authentication between user(s) and power generation nodes, and also mitigate against attacks by validating the identity during the authentication phase. Moreover, the proposed scheme, called Grid‐Based Authentication, uses the existing smart grid infrastructure for communications, which is a model of integrated infrastructure in a smart city. Furthermore, the simulation results show that our proposed scheme yields better performance in terms of average throughput, end to end delay, and computation and communication costs compared with other state‐of‐the‐art approaches.","PeriodicalId":506233,"journal":{"name":"SECURITY AND PRIVACY","volume":"104 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139801416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing 具有混合差分隐私的联盟学习，实现安全可靠的跨物联网平台知识共享

SECURITY AND PRIVACY Pub Date : 2024-02-03 DOI: 10.1002/spy2.374

Oshamah Ibrahim Khalaf, Ashokkumar S.R, Sameer Algburi, Anupallavi S, Dhanasekaran Selvaraj, M. S. Sharif, Wael Elmedany

{"title":"Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing","authors":"Oshamah Ibrahim Khalaf, Ashokkumar S.R, Sameer Algburi, Anupallavi S, Dhanasekaran Selvaraj, M. S. Sharif, Wael Elmedany","doi":"10.1002/spy2.374","DOIUrl":"https://doi.org/10.1002/spy2.374","url":null,"abstract":"The federated learning has gained prominent attention as a collaborative machine learning method, allowing multiple users to jointly train a shared model without directly exchanging raw data. This research addresses the fundamental challenge of balancing data privacy and utility in distributed learning by introducing an innovative hybrid methodology fusing differential privacy with federated learning (HDP‐FL). Through meticulous experimentation on EMNIST and CIFAR‐10 data sets, this hybrid approach yields substantial advancements, showcasing a noteworthy 4.22% and up to 9.39% enhancement in model accuracy for EMNIST and CIFAR‐10, respectively, compared to conventional federated learning methods. Our adjustments to parameters highlighted how noise impacts privacy, showcasing the effectiveness of our hybrid DP approach in striking a balance between privacy and accuracy. Assessments across diverse FL techniques and client counts emphasized this trade‐off, particularly in non‐IID data settings, where our hybrid method effectively countered accuracy declines. Comparative analyses against standard machine learning and state‐of‐the‐art FL approaches consistently showcased the superiority of our proposed model, achieving impressive accuracies of 96.29% for EMNIST and 82.88% for CIFAR‐10. These insights offer a strategic approach to securely collaborate and share knowledge among IoT devices without compromising data privacy, ensuring efficient and reliable learning mechanisms across decentralized networks.","PeriodicalId":506233,"journal":{"name":"SECURITY AND PRIVACY","volume":"26 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139808668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing 具有混合差分隐私的联盟学习，实现安全可靠的跨物联网平台知识共享

SECURITY AND PRIVACY Pub Date : 2024-02-03 DOI: 10.1002/spy2.374

Oshamah Ibrahim Khalaf, Ashokkumar S.R, Sameer Algburi, Anupallavi S, Dhanasekaran Selvaraj, M. S. Sharif, Wael Elmedany

{"title":"Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing","authors":"Oshamah Ibrahim Khalaf, Ashokkumar S.R, Sameer Algburi, Anupallavi S, Dhanasekaran Selvaraj, M. S. Sharif, Wael Elmedany","doi":"10.1002/spy2.374","DOIUrl":"https://doi.org/10.1002/spy2.374","url":null,"abstract":"The federated learning has gained prominent attention as a collaborative machine learning method, allowing multiple users to jointly train a shared model without directly exchanging raw data. This research addresses the fundamental challenge of balancing data privacy and utility in distributed learning by introducing an innovative hybrid methodology fusing differential privacy with federated learning (HDP‐FL). Through meticulous experimentation on EMNIST and CIFAR‐10 data sets, this hybrid approach yields substantial advancements, showcasing a noteworthy 4.22% and up to 9.39% enhancement in model accuracy for EMNIST and CIFAR‐10, respectively, compared to conventional federated learning methods. Our adjustments to parameters highlighted how noise impacts privacy, showcasing the effectiveness of our hybrid DP approach in striking a balance between privacy and accuracy. Assessments across diverse FL techniques and client counts emphasized this trade‐off, particularly in non‐IID data settings, where our hybrid method effectively countered accuracy declines. Comparative analyses against standard machine learning and state‐of‐the‐art FL approaches consistently showcased the superiority of our proposed model, achieving impressive accuracies of 96.29% for EMNIST and 82.88% for CIFAR‐10. These insights offer a strategic approach to securely collaborate and share knowledge among IoT devices without compromising data privacy, ensuring efficient and reliable learning mechanisms across decentralized networks.","PeriodicalId":506233,"journal":{"name":"SECURITY AND PRIVACY","volume":"55 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy‐preserving credit risk analysis based on homomorphic encryption aware logistic regression in the cloud 基于云中同态加密感知逻辑回归的隐私保护信用风险分析

SECURITY AND PRIVACY Pub Date : 2024-01-28 DOI: 10.1002/spy2.372

V. V. L. Divakar Allavarpu, V. Naresh, A. Krishna Mohan

{"title":"Privacy‐preserving credit risk analysis based on homomorphic encryption aware logistic regression in the cloud","authors":"V. V. L. Divakar Allavarpu, V. Naresh, A. Krishna Mohan","doi":"10.1002/spy2.372","DOIUrl":"https://doi.org/10.1002/spy2.372","url":null,"abstract":"With the growing significance of Credit Risk Analysis (CRA) with a focus on privacy, there is a pressing demand for a Privacy Preserving Machine Learning (PPML) decision support system. In this context, we introduce a framework for privacy‐preserving credit risk analysis that utilizes Homomorphic Encryption aware Logistic Regression (HELR) on encrypted data. The implementation involves the use of TenSEAL and Torch libraries for Logistic Regression (LR), integrating the proposed HELR on polynomial degrees 3 and 5 across German, Taiwan, Japan, and Australian datasets. The presented model yields satisfactory results compared to non‐Homomorphic Encryption (HE) models, demonstrating a minimal accuracy difference ranging from 0.5% to 7.8%. Notably, HELR_g5 outperforms HELR_g3, exhibiting a higher Area Under Curve (AUC) value. Additionally, a thorough security analysis indicates the resilience of the proposed system against various privacy attacks, including poison attacks, evasion attacks, member inference attacks, model inversion attacks, and model extraction attacks at different stages of machine learning. Finally, in the comparative analysis, we highlight that the proposed model ensures data privacy, encompassing training privacy and model privacy during the training phase, as well as input and output privacy during the inference phase a level of privacy not achieved by existing systems.","PeriodicalId":506233,"journal":{"name":"SECURITY AND PRIVACY","volume":"407 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140490648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MieWC: Medical image encryption using wavelet transform and multiple chaotic maps MieWC：利用小波变换和多重混沌图进行医学图像加密

SECURITY AND PRIVACY Pub Date : 2024-01-11 DOI: 10.1002/spy2.369

Kunal Demla, Ashima Anand

引用次数: 0