A Review on Security and Privacy Issues Pertaining to Cyber-Physical Systems in the Industry 5.0 Era

The advent of Industry 5.0 marks a transformative era where Cyber-Physical Systems(CPSs)seamlessly integrate physical processes with advanced digital technologies.However,as industries become increasingly interconnected and reliant on smart digital technologies,the intersection of physical and cyber domains introduces novel security considerations,endangering the entire industrial ecosystem.The transition towards a more cooperative setting,including humans and machines in Industry 5.0,together with the growing intricacy and interconnection of CPSs,presents distinct and diverse security and privacy challenges.In this regard,this study provides a comprehensive review of security and privacy concerns pertaining to CPSs in the context of Industry 5.0.The review commences by providing an outline of the role of CPSs in Industry 5.0 and then proceeds to conduct a thorough review of the different security risks associated with CPSs in the context of Industry 5.0.Afterward,the study also presents the privacy implications inherent in these systems,particularly in light of the massive data collection and processing required.In addition,the paper delineates potential avenues for future research and provides countermeasures to surmount these challenges.Overall,the study underscores the imperative of adopting comprehensive security and privacy strategies within the context of Industry 5.0.

Research on Residents’Willingness to Protect Privacy in the Context of the Personal Information Protection Law:A Survey Based on Foshan Residents’Data

The Personal Information Protection Law,as the first law on personal information protection in China,hits the people’s most concerned,realistic and direct privacy and information security issues,and plays an extremely important role in promoting the development of the digital economy,the legalization of socialism with Chinese characteristics and social public security,and marks a new historical development stage in the protection of personal information in China.However,the awareness of privacy protection and privacy protection behavior of the public in personal information privacy protection is weak.Based on the literature review and in-depth understanding of current legal regulations,this study integrates the relevant literature and theoretical knowledge of the Personal Protection Law to construct a conceptual model of“privacy information protection willingness-privacy information protection behavior”.Taking the residents of Foshan City as an example,this paper conducts a questionnaire survey on their attitudes toward the Personal Protection Law,analyzes the factors influencing their willingness to protect their privacy and their behaviors,and explores the mechanisms of their influencing variables,to provide advice and suggestions for promoting the protection of privacy information and building a security barrier for the high-quality development of public information security.

Interpretation of Information Security and Data Privacy Protection According to the Data Use During the Epidemic

COVID-19 has swept the whole our country and the world in the beginning of 2020.31 provinces and municipalities across the country have launched the first-level response to major public health emergencies since January 24,and China has carried out intensive epidemic control.It is critical for effectively responding to COVID-19 to collect,collate and analyze people’s personal data.What’s more,obtaining identity information,travel records and health information of confirmed cases,suspected cases and close contacts has become a crucial step in epidemic investigation.All regions have made full use of big data to carry out personnel screening,travel records analysis and other related work in epidemic prevention and control,effectively improving the efficiency of epidemic prevention and control.However,data leakage,personnel privacy data exposure,and personal attack frequently occurred in the process of personnel travel records analysis and epidemic prevention and control.It even happened in the WeChat group to forward a person’s name,phone number,address,ID number and other sensitive information.It brought discrimination,telephone and SMS harassment to the parties,which caused great harm to individuals.Based on these,lack of information security and data security awareness and other issues were exposed.Therefore,while big data has been widely concerned and applied,attention should be paid to protecting personal privacy.It is urgent to pay more attention to data privacy and information security in order to effectively protect the legitimate rights of the people.Therefore,measures can be taken to achieve this goal,such as improving the relevant legal system,strengthening technical means to enhance the supervision and management of information security and data protection.

Changes and Adjustments:The Rule of Law Response to Medical Institution Data Compliance

Medical institution data compliance is an exogenous product of the digital society,serving as a crucial means to maintain and balance the relationship between data protection and data sharing,as well as individual interests and public interests.The implementation of the Healthy China Initiative greatly benefits from its practical significance.In practice,data from medical institutions takes varied forms,including personally identifiable data collected before diagnosis and treatment,clinical medical data generated during diagnosis and treatment,medical data collected in public health management,and potential medical data generated in daily life.In the new journey of comprehensively promoting the Chinese path to modernization,it is necessary to clarify the shift from an individual-oriented to a societal-oriented value system,highlighting the reinforcing role of the trust concept.Guided by the principle of minimizing data utilization,the focus is on the new developments and changes in medical institution data in the postpandemic era.This involves a series of measures such as fulfilling the obligation of notification and consent,specifying the scope of data collection and usage,strengthening the standardized use of relevant technical measures,and establishing a sound legal responsibility system for data compliance.Through these measures,a flexible and efficient medical institution data compliance system can be constructed.

A Literature Review: Potential Effects That Health Apps on Mobile Devices May Have on Patient Privacy and Confidentiality

Purpose: This research aims to evaluate the potential threats to patient privacy and confidentiality posed by mHealth applications on mobile devices. Methodology: A comprehensive literature review was conducted, selecting eighty-eight articles published over the past fifteen years. The study assessed data gathering and storage practices, regulatory adherence, legal structures, consent procedures, user education, and strategies to mitigate risks. Results: The findings reveal significant advancements in technologies designed to safeguard privacy and facilitate the widespread use of mHealth apps. However, persistent ethical issues related to privacy remain largely unchanged despite these technological strides.

Privacy Cost Analysis and Privacy Protection Based on Big Data

A comprehensive analysis of the impact privacy incidents on its market value is given.A broad set of instances of the exposure of personal information from a summary of some security mechanisms and the corresponding results are presented. The cumulative effect increases in magnitude over day following the breach announcement, but then decreases. Besides, a new privacy protection property, that is, p-sensitive k-anonymity is presented in this paper to protect against identity disclosure. We illustrated the inclusion of the two necessary conditions in the algorithm for computing a p-k-minimal generalization. Algorithms such as k-anonymity and l-diversity remain all sensitive attributes intact and apply generalization and suppression to the quasi-identifiers. This will keep the data "truthful" and provide good utility for data-mining applications, while achieving less perfect privacy. We aim to get the problem based on the prior analysis, and study the issue of privacy protection from the perspective of the model-benefit.

VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Cyber Security-Protecting Personal Data

Many organizations have datasets which contain a high volume of personal data on individuals,e.g.,health data.Even without a name or address,persons can be identified based on the details(variables)on the dataset.This is an important issue for big data holders such as public sector organizations(e.g.,Public Health Organizations)and social media companies.This paper looks at how individuals can be identified from big data using a mathematical approach and how to apply this mathematical solution to prevent accidental disclosure of a person’s details.The mathematical concept is known as the“Identity Correlation Approach”(ICA)and demonstrates how an individual can be identified without a name or address using a unique set of characteristics(variables).Secondly,having identified the individual person,it shows how a solution can be put in place to prevent accidental disclosure of the personal details.Thirdly,how to store data such that accidental leaks of the datasets do not lead to the disclosure of the personal details to unauthorized users.

A New Anonymity Model for Privacy-Preserving Data Publishing

Privacy-preserving data publishing （PPDP） is one of the hot issues in the field of the network security. The existing PPDP technique cannot deal with generality attacks, which explicitly contain the sensitivity attack and the similarity attack. This paper proposes a novel model, （w,γ, k）-anonymity, to avoid generality attacks on both cases of numeric and categorical attributes. We show that the optimal （w, γ, k）-anonymity problem is NP-hard and conduct the Top-down Local recoding （TDL） algorithm to implement the model. Our experiments validate the improvement of our model with real data.

基于联邦学习的个性化推荐系统研究

为了通过联邦学习算法解决用户隐私数据泄露的问题、降低数据泄露的可能性,本文概述了推荐系统、联邦学习及联邦推荐系统,探讨了联邦个性化推荐系统的类别、流程、应用现状以及未来面对的挑战等,为用户提供了安全、便捷、高效的个性化推荐系统。

欧盟《数据法案》的规范要旨与制度启示:以个人信息保护为视角

欧洲议会于2023年11月9日表决通过《数据法案》。该法案明确符合欧盟价值观的数据流转利用和数据治理规则,保障欧洲单一数据市场中数据要素的安全高效流动,进一步平衡个人数据保护和数据自由流通之间的关系。在数字时代下,我国可以在个人信息权益保护、统一协调的数据治理机制建立、技术互联与标准建设强化、数据安全保护监管优化等方面吸收借鉴其经验,在充分释放数据要素价值的同时,进一步完善数字时代数据安全与个人信息保护标准,规范数据的共享流通,保障数字经济安全有序发展。

国家安全观视域下的美国数据隐私框架探析

[目的/意义]从国家安全观视角,不仅能考察美国数据隐私保护框架的顶层设计,也能充分、全面、立体地揭示出美国数据隐私保护制度的整体趋势与优劣,为我国跨境数字贸易流通有效安全政策的制定提供理论依据。[方法/过程]运用文献研究与内容分析法,对美国新型国家安全观的基本构成与特点、数据隐私立法演化历程及其数据隐私框架进行了分析。[结果/结论]美国新型国家安全观下的数据隐私保护以数字经济优先发展为前提;其数据隐私整体框架,由国内和跨境数据保护与流动规则两个部分组成;美国数据隐私保护统一立法、跨境数据流动监管与治理现代化立法成为趋势;美国主导的同盟国跨境数据流动圈的形成,成为逆全球化的数据贸易壁垒;构建“共同价值观下的网络空间命运共同体”成为中国的必然选择。

个人数据保护和公共利益平衡的实证研究

在全球范围内,个人数据保护与公共利益的维护已经成为一个备受关注的重要法律问题。本文通过实证研究,对各国和地区关于个人数据保护的立法及其在数据立法中对公共利益的立场进行了深入调查和分析。研究发现,不论是在法规制定还是在实际执行中,关于如何平衡个人数据保护和公共利益的关系仍然存在提升的空间。为此,本文提出了一个新的理论框架,重新解释了公共利益的概念,并通过实证检验了这一理论框架的可行性。该框架旨在为平衡个人数据保护和公共利益提供一种创新的方法。通过深度剖析不同国家和地区的现行实践,以及在法规制定中对公共利益考量的差异,该理论框架试图在尊重个人隐私的前提下,促进更加有效、公正和全面的数据治理。

美国数据泄露通知制度研究

为应对日益频繁的数据泄露事件,具有及时止损功能的数据泄露通知制度逐渐受到各法治国家的重视。美国是世界上最早确立数据泄露通知制度的国家,截至2018年,其50个州均颁布了数据泄露通知法,内容涉及触发条件、通知流程、处罚机制等诸多方面。通过对美国各州立法的考察,可以为我国的数据泄露通知制度立法提供借鉴。在立法模式层面,国家应出台详尽的数据泄露通知规范;在立法理念层面,应在保护消费者权益的同时兼顾数据处理者的利益;在立法技术层面,应当从触发条件、通知流程、处罚机制等方面制定系统完善的数据泄露通知规则。

大数据背景下网络安全与隐私保护技术研究

随着互联网和大数据技术的发展,网络安全和个人隐私面临着空前的威胁。文章主要介绍大数据、网络安全与个人隐私内容,分析大数据背景下网络安全与隐私保护问题,并探讨大数据背景下网络安全与隐私保护相关的技术,希望对维护网络安全和保护用户隐私有所帮助。

基于区块链隐私保护的工业网络输出指令安全控制算法设计

当前工业网络输出指令加密机制多为目标式和独立式,导致指令安全控制效果不理想,丢包率增加,为此提出基于区块链隐私保护的工业网络输出指令安全控制算法。设计工业网络指令安全控制辨识目标,将此目标导入初始的安全指令控制加密程序中,设置一个基础的控制指令。以其作为引导,进行安全控制目标的扩展及生成,对指令的内容进行多层级加密。在指令加密处理的基础上,根据输出指令安全控制指标及参数的限制,设计不同的安全控制标准和双向控制条件,将安全控制辨识目标导入当前的区块链双向控制结构中,形成一个具体细化的控制结构。在控制结构中,利用区块链技术将区块控制程序与重叠指令的控制程序关联起来,实现动态化的指令输出安全控制。实验结果表明:这种安全控制算法能够对全部工业网络输出指令数据进行加密处理,能够最大程度上保证工业网络输出指令隐私安全,丢包率在5.2%以下,控制正确率较高,具有较高的实际应用价值。

隐私何以让渡:量化自我与私人数据的日常实践

随着“量化自我”的兴起,让渡隐私成为与自我监测伴生的显著问题,呈现出功能性的悖论。立足于此,对该群体的质性研究发现:受数字化生存惯习的影响,量化自我群体的隐私感经历着技术社会的“脱敏”,让渡数据变得习以为常;而在使用过程中,量化自我从多样的情境中形成让渡有益的行为理念,从而将数据监控化用为自我跟踪;最终,隐私关注与授权超越了“是否构成/侵犯隐私”的规制界定,以“如何私密”的实践策略反映出对私人数据的选择和管理。鉴于此,理解数字社会的隐私让渡需要从侵权范式回到“隐私与关系”之中,在动态的实践视角下探寻平衡之道。

个性化联邦学习的相关方法与展望

目前,随着人工智能研究的进步,人工智能被大规模采用,数据监管等领域的需求也促使人们对隐私保护的认识和关注越来越多,这促进了联邦学习(federated learning,FL)框架的流行。但现有的FL难以应对异构问题以及用户的个性化需求。为了应对上述问题,研究了个性化联邦学习(personalized federated learning,PFL)的相关方法并提出了展望。列举了FL的框架并指出了FL的不足,在FL场景的基础上,引出PFL的研究动机对PFL中的统计异构、模型异构、通信异构、设备异构进行分析并提出可行性方案;将PFL中的客户端选择、知识蒸馏等个性化算法分类并分析各自的创新与不足。最后,对PFL的未来研究方向进行了展望。

高效的隐私保护多方多数据排序

安全多方计算允许具有私密输入的多个参与方联合计算一个多输入函数而不泄露各参与方私有输入的任何信息,因此近年来受到广泛关注.作为安全多方计算中的一个基础问题,隐私保护排序允许多个参与方在不泄露数据集隐私的前提下计算多个数据集的排序结果,广泛应用于产品定价、拍卖等场景.现有的隐私保护排序协议大多只支持两个参与方.而已有的多方多数据排序协议通信开销大、计算复杂度高,整体效率较低.现有隐私保护排序协议均未考虑恶意参与者的穷举攻击,因此安全保护不足.对此,本文提出一个高效的隐私保护多方多数据排序协议.多个参与方仅需O(1)轮交互即可以隐私保护的方式获得其持有的多个数据的排序结果.具体来讲,本文设计一种基于多项式的编码方法,将参与方的数据集编码为一个多项式,其每项的指数和系数分别代表数据和该数据的个数.通过多项式加法可实现多个参与方数据集的排序.同时,本文设计了多项式加密、聚合多项式生成和解密多项式生成算法,在保证计算正确性的同时实现多项式的隐私保护.最后,各参与方通过不经意传输技术获得排序结果.本文定义了不合谋参与方穷举攻击下的恶意安全.安全性分析表明本文协议不仅实现了半诚实安全性,而且达到了不合谋恶意用户穷举攻击的恶意安全性.此外,大量实验表明本文提出的协议在通信和计算方面都十分高效.如当参与方数量为15、每个参与方持有20000个数据、数据上界为500000时,本文协议的通信和计算开销分别为898.44 MB和69.76 s,仅为LDYW协议的12.08%和76.85%;而相对于AHM+方案,本文协议在通信开销仅增加约4倍的情况下使计算效率提升了约20倍.