To cope with the challenges of CoViD-19,europe has adopted relevant measures of a data-based approach to governance,on which scholars have huge differences,and the related researches are conducive to further discussio...To cope with the challenges of CoViD-19,europe has adopted relevant measures of a data-based approach to governance,on which scholars have huge differences,and the related researches are conducive to further discussion on the differences.By sorting out the challenges posed by the pandemic to public security and data protection in europe,we can summarize the“european Solution”of the data-based approach to governance,including legislation,instruments,supervision,international cooperation,and continuity.The“Solution”has curbed the spread of the pandemic to a certain extent.However,due to the influence of the traditional values of the EU,the“Solution”is too idealistic in the balance between public security and data protection,which intensifies the dilemma and causes many problems,such as ambiguous legislation,inadequate effectiveness and security of instruments,an arduous endeavor in inter national cooperation,and imperfect regulations on digital green certificates.Therefore,in a major public health crisis,there is still a long way to go in exploring a balance between public security and data protection.展开更多
Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemin...Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.展开更多
Most studies have conducted experiments on predicting energy consumption by integrating data formodel training.However, the process of centralizing data can cause problems of data leakage.Meanwhile,many laws and regul...Most studies have conducted experiments on predicting energy consumption by integrating data formodel training.However, the process of centralizing data can cause problems of data leakage.Meanwhile,many laws and regulationson data security and privacy have been enacted, making it difficult to centralize data, which can lead to a datasilo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework.However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg)method is used to directly weight the model parameters on average, which may have an adverse effect on te model.Therefore, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple userscollaboratively training the model. Each household trains a local model on local data. These local data neverleave the local area, and only the encrypted parameters are uploaded to the central server to participate in thesecure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assignweights to each locally uploaded local model. And the model has improved predictive performance. We validatethe performance of the FedRL model by testing it on a real-world dataset and compare the experimental results withother models. The performance of our proposed method in most of the evaluation metrics is improved comparedto both the centralized and distributed models.展开更多
Customer privacy perception and the principles of its regulatory protection determine how the tech sector is operating,striking a new balance between economic winners and losers.Nevertheless,not all countries that are...Customer privacy perception and the principles of its regulatory protection determine how the tech sector is operating,striking a new balance between economic winners and losers.Nevertheless,not all countries that are leaders in the latest technologies are strongly in favor of flexible and pro-business regulations.This can be clearly seen in the field of artificial intelligence(AI).Self-regulation as a key strategic approach to AI may be seen as an essential factor of broader implementation of AI solutions.The purpose of this paper is to present approaches to AI while indicating the differences that result from the understanding of privacy,increasing customers privacy concerns and regulations related to data privacy which come together with official administrative strategies.The impact of AI implementation on relationships between customers and companies has been emphasized and analyzed in the context of regulations and customer perception of privacy.展开更多
In Trust Zone architecture, the Trusted Application（TA） in the secure world does not certify the identity of Client Applications（CA） in the normal world that request data access, which represents a user data leaka...In Trust Zone architecture, the Trusted Application（TA） in the secure world does not certify the identity of Client Applications（CA） in the normal world that request data access, which represents a user data leakage risk. This paper proposes a private user data protection mechanism in Trust Zone to avoid such risks. We add corresponding modules to both the secure world and the normal world and authenticate the identity of CA to prevent illegal access to private user data. Then we analyze the system security, and perform validity and performance tests.The results show that this method can perform effective identity recognition and control of CA to protect the security of private user data. After adding authentication modules, the data operation time of system increases by about0.16 s, an acceptable price to pay for the improved security.展开更多
This paper rethinks the reasons for and the nature and means of personal data protection. The reasons for personal data protection are that it could promote the fairness and effectiveness of information flow, help ind...This paper rethinks the reasons for and the nature and means of personal data protection. The reasons for personal data protection are that it could promote the fairness and effectiveness of information flow, help individuals develop their independent personality, and equip them to deal with risks. With respect to the nature of personal data, this paper argues that such data should not be perceived from a purely individualistic point of view. Rather, there should be a contextualized understanding of the data, which considers the appropriate information flow of personal data within a particular context. Regarding the legal framework of personal data protection, this paper suggests that consumer protection law and public law are better equipped to protect personal data than tort, contract, or property law.展开更多
Platform data has already become an important asset for web-based companies,but this sort of data frequently includes large amounts of personal information.Platform data can be seen as belonging to an individual,belon...Platform data has already become an important asset for web-based companies,but this sort of data frequently includes large amounts of personal information.Platform data can be seen as belonging to an individual,belonging to a platform,belonging to some combinations of the two,or can be seen as a form of Internet-based public data.Analysis of legal clauses and doctrines as well as analysis based in legitimacy and consequentialism both fail to completely delineate data ownership.One potential reason for this is that there are many types of platform data,and that each type is highly dependent on circumstances.The determination of rights in regard to platform data should be done in a way which revolves around a contextual regulatory framework,one in which the rules of reason is applied on a case-by-case basis and in which gradual changes are done in a bottom-up manner,and not one which seeks to establish a universal set of data regulations.In actual judgments,factors such as the nature of the platform and the nature of the data crawling behavior should be comprehensively considered while ensuring a balance of data circulation and data protection.展开更多
As Vehicular ad hoc networks (VANETs) become more sophisticated, the importance of integrating data protection and cybersecurity is increasingly evident. This paper offers a comprehensive investigation into the challe...As Vehicular ad hoc networks (VANETs) become more sophisticated, the importance of integrating data protection and cybersecurity is increasingly evident. This paper offers a comprehensive investigation into the challenges and solutions associated with the privacy implications within VANETs, rooted in an intricate landscape of cross-jurisdictional data protection regulations. Our examination underscores the unique nature of VANETs, which, unlike other ad-hoc networks, demand heightened security and privacy considerations due to their exposure to sensitive data such as vehicle identifiers, routes, and more. Through a rigorous exploration of pseudonymization schemes, with a notable emphasis on the Density-based Location Privacy (DLP) method, we elucidate the potential to mitigate and sometimes sidestep the heavy compliance burdens associated with data protection laws. Furthermore, this paper illuminates the cybersecurity vulnerabilities inherent to VANETs, proposing robust countermeasures, including secure data transmission protocols. In synthesizing our findings, we advocate for the proactive adoption of protective mechanisms to facilitate the broader acceptance of VANET technology while concurrently addressing regulatory and cybersecurity hurdles.展开更多
Neutral beam injection is one of the effective auxiliary heating methods in magnetic-confinementfusion experiments. In order to acquire the suppressor-grid current signal and avoid the grid being damaged by overheatin...Neutral beam injection is one of the effective auxiliary heating methods in magnetic-confinementfusion experiments. In order to acquire the suppressor-grid current signal and avoid the grid being damaged by overheating, a data acquisition and over-current protection system based on the PXI（PCI e Xtensions for Instrumentation） platform has been developed. The system consists of a current sensor, data acquisition module and over-current protection module. In the data acquisition module,the acquired data of one shot will be transferred in isolation and saved in a data-storage server in a txt file. It can also be recalled using NBWave for future analysis. The over-current protection module contains two modes： remote and local. This gives it the function of setting a threshold voltage remotely and locally, and the forbidden time of over-current protection also can be set by a host PC in remote mode. Experimental results demonstrate that the data acquisition and overcurrent protection system has the advantages of setting forbidden time and isolation transmission.展开更多
The right to the protection of personal data is an important human right in the era of big data and a constitutional right based on the national protection obligation and the theory of human dignity,making it of speci...The right to the protection of personal data is an important human right in the era of big data and a constitutional right based on the national protection obligation and the theory of human dignity,making it of special significance for the realization of citizenship in a digital society.It can be seen from an examination of the constitutional texts of various countries in the world that the right to the protection of personal data as a constitutional right has rich normative connotations,and the key legal link to realize this right lies in the national legislature actively fulfilling its obligation to shape and specify the protection of personal data in accordance with the entrustment of the constitutional norms.Given the constitutional principles of fundamental rights protection,i.e.,realizing the constitutional status of the right to the protection of personal data as a basic right by means of institutional guarantees,the legislature should first adhere to the constitutionality principle of data protection legislation.Second,a multi-level data protection legal system centered on the right to the protection of personal data should be established.Finally,the institutional guarantee mechanism for the protection of personal data should be continuously improved through constitutional interpretation.展开更多
Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first prop...Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this paper.Here,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its performance.Then,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation platforms.Subsequently,it is used toward secure communication application scenarios.Taking it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)model.Eventually,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.展开更多
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 interconnect...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.展开更多
In the era of internet proliferation,safeguarding digital media copyright and integrity,especially for images,is imperative.Digital watermarking stands out as a pivotal solution for image security.With the advent of d...In the era of internet proliferation,safeguarding digital media copyright and integrity,especially for images,is imperative.Digital watermarking stands out as a pivotal solution for image security.With the advent of deep learning,watermarking has seen significant advancements.Our review focuses on the innovative deep watermarking approaches that employ neural networks to identify robust embedding spaces,resilient to various attacks.These methods,characterized by a streamlined encoder-decoder architecture,have shown enhanced performance through the incorporation of novel training modules.This article offers an in-depth analysis of deep watermarking’s core technologies,current status,and prospective trajectories,evaluating recent scholarly contributions across diverse frameworks.It concludes with an overview of the technical hurdles and prospects,providing essential insights for ongoing and future research endeavors in digital image watermarking.展开更多
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, selec...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.展开更多
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 t...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].展开更多
This paper conducts a comprehensive review of existing research on Privacy by Design (PbD) and behavioral economics, explores the intersection of Privacy by Design (PbD) and behavioral economics, and how designers can...This paper conducts a comprehensive review of existing research on Privacy by Design (PbD) and behavioral economics, explores the intersection of Privacy by Design (PbD) and behavioral economics, and how designers can leverage “nudges” to encourage users towards privacy-friendly choices. We analyze the limitations of rational choice in the context of privacy decision-making and identify key opportunities for integrating behavioral economics into PbD. We propose a user-centered design framework for integrating behavioral economics into PbD, which includes strategies for simplifying complex choices, making privacy visible, providing feedback and control, and testing and iterating. Our analysis highlights the need for a more nuanced understanding of user behavior and decision-making in the context of privacy, and demonstrates the potential of behavioral economics to inform the design of more effective PbD solutions.展开更多
Starting from the importance of preserving our information and protecting our devices against attacks on their vulnerabilities, this article aims to establish the level of knowledge in computer security and problem-so...Starting from the importance of preserving our information and protecting our devices against attacks on their vulnerabilities, this article aims to establish the level of knowledge in computer security and problem-solving of students at a university in northwest Mexico. This research has a quantitative approach where the European Framework of Digital Competencies for Citizenship was used as a reference to identify and establish the level of the competencies that were evaluated. The IKANOS Test was used as a data collection tool. The results show that students know the importance of keeping their devices safe and how valuable the information found on them is. On the other hand, the results also show a considerable percentage of students who do not have the knowledge and are at a basic level of knowledge to solve technical problems with their devices.展开更多
Open-source licenses can promote the development of machine learning by allowing others to access,modify,and redistribute the training dataset.However,not all open-source licenses may be appropriate for data sharing,a...Open-source licenses can promote the development of machine learning by allowing others to access,modify,and redistribute the training dataset.However,not all open-source licenses may be appropriate for data sharing,as some may not provide adequate protections for sensitive or personal information such as social network data.Additionally,some data may be subject to legal or regulatory restrictions that limit its sharing,regardless of the licensing model used.Hence,obtaining large amounts of labeled data can be difficult,time-consuming,or expensive in many real-world scenarios.Few-shot graph classification,as one application of meta-learning in supervised graph learning,aims to classify unseen graph types by only using a small amount of labeled data.However,the current graph neural network methods lack full usage of graph structures on molecular graphs and social network datasets.Since structural features are known to correlate with molecular properties in chemistry,structure information tends to be ignored with sufficient property information provided.Nevertheless,the common binary classification task of chemical compounds is unsuitable in the few-shot setting requiring novel labels.Hence,this paper focuses on the graph classification tasks of a social network,whose complex topology has an uncertain relationship with its nodes'attributes.With two multi-class graph datasets with large node-attribute dimensions constructed to facilitate the research,we propose a novel learning framework that integrates both meta-learning and contrastive learning to enhance the utilization of graph topological information.Extensive experiments demonstrate the competitive performance of our framework respective to other state-of-the-art methods.展开更多
With the wide application of the Internet of Things(IoT),storing large amounts of IoT data and protecting data privacy has become a meaningful issue.In general,the access control mechanism is used to prevent illegal u...With the wide application of the Internet of Things(IoT),storing large amounts of IoT data and protecting data privacy has become a meaningful issue.In general,the access control mechanism is used to prevent illegal users from accessing private data.However,traditional data access control schemes face some non-ignorable problems,such as only supporting coarse-grained access control,the risk of centralization,and high trust issues.In this paper,an attribute-based data access control scheme using blockchain technology is proposed.To address these problems,attribute-based encryption(ABE)has become a promising solution for encrypted data access control.Firstly,we utilize blockchain technology to construct a decentralized access control scheme,which can grant data access with transparency and traceability.Furthermore,our scheme also guarantees the privacy of policies and attributes on the blockchain network.Secondly,we optimize an ABE scheme,which makes the size of system parameters smaller and improves the efficiency of algorithms.These optimizations enable our proposed scheme supports large attribute universe requirements in IoT environments.Thirdly,to prohibit attribute impersonation and attribute replay attacks,we design a challenge-response mechanism to verify the ownership of attributes.Finally,we evaluate the security and performance of the scheme.And comparisons with other related schemes show the advantages of our proposed scheme.Compared to existing schemes,our scheme has more comprehensive advantages,such as supporting a large universe,full security,expressive policy,and policy hiding.展开更多
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...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.展开更多
基金the phased achievement of the major research project of the National Social Science Fund of China(Project Approval No.21VGQ010)supported by the 2021 Central University Basic Scientific Research Project of Lanzhou University(Project Approval No.21lzujbkyjd002).
文摘To cope with the challenges of CoViD-19,europe has adopted relevant measures of a data-based approach to governance,on which scholars have huge differences,and the related researches are conducive to further discussion on the differences.By sorting out the challenges posed by the pandemic to public security and data protection in europe,we can summarize the“european Solution”of the data-based approach to governance,including legislation,instruments,supervision,international cooperation,and continuity.The“Solution”has curbed the spread of the pandemic to a certain extent.However,due to the influence of the traditional values of the EU,the“Solution”is too idealistic in the balance between public security and data protection,which intensifies the dilemma and causes many problems,such as ambiguous legislation,inadequate effectiveness and security of instruments,an arduous endeavor in inter national cooperation,and imperfect regulations on digital green certificates.Therefore,in a major public health crisis,there is still a long way to go in exploring a balance between public security and data protection.
基金funded by the High-Quality and Cutting-Edge Discipline Construction Project for Universities in Beijing (Internet Information,Communication University of China).
文摘Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.
基金supported by National Key R&D Program of China(No.2020YFC2006602)National Natural Science Foundation of China(Nos.62172324,62072324,61876217,6187612)+2 种基金University Natural Science Foundation of Jiangsu Province(No.21KJA520005)Primary Research and Development Plan of Jiangsu Province(No.BE2020026)Natural Science Foundation of Jiangsu Province(No.BK20190942).
文摘Most studies have conducted experiments on predicting energy consumption by integrating data formodel training.However, the process of centralizing data can cause problems of data leakage.Meanwhile,many laws and regulationson data security and privacy have been enacted, making it difficult to centralize data, which can lead to a datasilo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework.However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg)method is used to directly weight the model parameters on average, which may have an adverse effect on te model.Therefore, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple userscollaboratively training the model. Each household trains a local model on local data. These local data neverleave the local area, and only the encrypted parameters are uploaded to the central server to participate in thesecure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assignweights to each locally uploaded local model. And the model has improved predictive performance. We validatethe performance of the FedRL model by testing it on a real-world dataset and compare the experimental results withother models. The performance of our proposed method in most of the evaluation metrics is improved comparedto both the centralized and distributed models.
文摘Customer privacy perception and the principles of its regulatory protection determine how the tech sector is operating,striking a new balance between economic winners and losers.Nevertheless,not all countries that are leaders in the latest technologies are strongly in favor of flexible and pro-business regulations.This can be clearly seen in the field of artificial intelligence(AI).Self-regulation as a key strategic approach to AI may be seen as an essential factor of broader implementation of AI solutions.The purpose of this paper is to present approaches to AI while indicating the differences that result from the understanding of privacy,increasing customers privacy concerns and regulations related to data privacy which come together with official administrative strategies.The impact of AI implementation on relationships between customers and companies has been emphasized and analyzed in the context of regulations and customer perception of privacy.
基金supported by the National HighTech Research and Development (863) Program (No. 2015AA016002)the National Key Basic Research Program of China (No. 2014CB340600)+1 种基金the National Natural Science Foundation of China (Nos. 61303024 and 61272452)the Natural Science Foundation of Jiangsu Province (Nos. BK20130372)
文摘In Trust Zone architecture, the Trusted Application（TA） in the secure world does not certify the identity of Client Applications（CA） in the normal world that request data access, which represents a user data leakage risk. This paper proposes a private user data protection mechanism in Trust Zone to avoid such risks. We add corresponding modules to both the secure world and the normal world and authenticate the identity of CA to prevent illegal access to private user data. Then we analyze the system security, and perform validity and performance tests.The results show that this method can perform effective identity recognition and control of CA to protect the security of private user data. After adding authentication modules, the data operation time of system increases by about0.16 s, an acceptable price to pay for the improved security.
文摘This paper rethinks the reasons for and the nature and means of personal data protection. The reasons for personal data protection are that it could promote the fairness and effectiveness of information flow, help individuals develop their independent personality, and equip them to deal with risks. With respect to the nature of personal data, this paper argues that such data should not be perceived from a purely individualistic point of view. Rather, there should be a contextualized understanding of the data, which considers the appropriate information flow of personal data within a particular context. Regarding the legal framework of personal data protection, this paper suggests that consumer protection law and public law are better equipped to protect personal data than tort, contract, or property law.
基金This paper is a periodic achievement of two projects of the National Social Science Fund of China,those being the major project:“Personal Data Protection and Data Rights Systems in the Age of Big Data”(Project No.18ZDA146)the general project:“Research on Personal Information Protection and Corporate Data Ownership in the Context of Big Data”(Project No.18BFX198)。
文摘Platform data has already become an important asset for web-based companies,but this sort of data frequently includes large amounts of personal information.Platform data can be seen as belonging to an individual,belonging to a platform,belonging to some combinations of the two,or can be seen as a form of Internet-based public data.Analysis of legal clauses and doctrines as well as analysis based in legitimacy and consequentialism both fail to completely delineate data ownership.One potential reason for this is that there are many types of platform data,and that each type is highly dependent on circumstances.The determination of rights in regard to platform data should be done in a way which revolves around a contextual regulatory framework,one in which the rules of reason is applied on a case-by-case basis and in which gradual changes are done in a bottom-up manner,and not one which seeks to establish a universal set of data regulations.In actual judgments,factors such as the nature of the platform and the nature of the data crawling behavior should be comprehensively considered while ensuring a balance of data circulation and data protection.
文摘As Vehicular ad hoc networks (VANETs) become more sophisticated, the importance of integrating data protection and cybersecurity is increasingly evident. This paper offers a comprehensive investigation into the challenges and solutions associated with the privacy implications within VANETs, rooted in an intricate landscape of cross-jurisdictional data protection regulations. Our examination underscores the unique nature of VANETs, which, unlike other ad-hoc networks, demand heightened security and privacy considerations due to their exposure to sensitive data such as vehicle identifiers, routes, and more. Through a rigorous exploration of pseudonymization schemes, with a notable emphasis on the Density-based Location Privacy (DLP) method, we elucidate the potential to mitigate and sometimes sidestep the heavy compliance burdens associated with data protection laws. Furthermore, this paper illuminates the cybersecurity vulnerabilities inherent to VANETs, proposing robust countermeasures, including secure data transmission protocols. In synthesizing our findings, we advocate for the proactive adoption of protective mechanisms to facilitate the broader acceptance of VANET technology while concurrently addressing regulatory and cybersecurity hurdles.
基金supported by National Natural Science Foundation of China(No.11575240)Key Program of Research and Development of Hefei Science Center,CAS(grant 2016HSC-KPRD002)
文摘Neutral beam injection is one of the effective auxiliary heating methods in magnetic-confinementfusion experiments. In order to acquire the suppressor-grid current signal and avoid the grid being damaged by overheating, a data acquisition and over-current protection system based on the PXI（PCI e Xtensions for Instrumentation） platform has been developed. The system consists of a current sensor, data acquisition module and over-current protection module. In the data acquisition module,the acquired data of one shot will be transferred in isolation and saved in a data-storage server in a txt file. It can also be recalled using NBWave for future analysis. The over-current protection module contains two modes： remote and local. This gives it the function of setting a threshold voltage remotely and locally, and the forbidden time of over-current protection also can be set by a host PC in remote mode. Experimental results demonstrate that the data acquisition and overcurrent protection system has the advantages of setting forbidden time and isolation transmission.
基金the provincial key academic project Research of the Grassroots Negotiation and Governance Modernization Viewing from the Angle of State Governance(2019-GDXK-0005)
文摘The right to the protection of personal data is an important human right in the era of big data and a constitutional right based on the national protection obligation and the theory of human dignity,making it of special significance for the realization of citizenship in a digital society.It can be seen from an examination of the constitutional texts of various countries in the world that the right to the protection of personal data as a constitutional right has rich normative connotations,and the key legal link to realize this right lies in the national legislature actively fulfilling its obligation to shape and specify the protection of personal data in accordance with the entrustment of the constitutional norms.Given the constitutional principles of fundamental rights protection,i.e.,realizing the constitutional status of the right to the protection of personal data as a basic right by means of institutional guarantees,the legislature should first adhere to the constitutionality principle of data protection legislation.Second,a multi-level data protection legal system centered on the right to the protection of personal data should be established.Finally,the institutional guarantee mechanism for the protection of personal data should be continuously improved through constitutional interpretation.
文摘Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this paper.Here,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its performance.Then,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation platforms.Subsequently,it is used toward secure communication application scenarios.Taking it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)model.Eventually,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.62072465,62102425)the Science and Technology Innovation Program of Hunan Province(Nos.2022RC3061,2023RC3027).
文摘In the era of internet proliferation,safeguarding digital media copyright and integrity,especially for images,is imperative.Digital watermarking stands out as a pivotal solution for image security.With the advent of deep learning,watermarking has seen significant advancements.Our review focuses on the innovative deep watermarking approaches that employ neural networks to identify robust embedding spaces,resilient to various attacks.These methods,characterized by a streamlined encoder-decoder architecture,have shown enhanced performance through the incorporation of novel training modules.This article offers an in-depth analysis of deep watermarking’s core technologies,current status,and prospective trajectories,evaluating recent scholarly contributions across diverse frameworks.It concludes with an overview of the technical hurdles and prospects,providing essential insights for ongoing and future research endeavors in digital image watermarking.
文摘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.
文摘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].
文摘This paper conducts a comprehensive review of existing research on Privacy by Design (PbD) and behavioral economics, explores the intersection of Privacy by Design (PbD) and behavioral economics, and how designers can leverage “nudges” to encourage users towards privacy-friendly choices. We analyze the limitations of rational choice in the context of privacy decision-making and identify key opportunities for integrating behavioral economics into PbD. We propose a user-centered design framework for integrating behavioral economics into PbD, which includes strategies for simplifying complex choices, making privacy visible, providing feedback and control, and testing and iterating. Our analysis highlights the need for a more nuanced understanding of user behavior and decision-making in the context of privacy, and demonstrates the potential of behavioral economics to inform the design of more effective PbD solutions.
文摘Starting from the importance of preserving our information and protecting our devices against attacks on their vulnerabilities, this article aims to establish the level of knowledge in computer security and problem-solving of students at a university in northwest Mexico. This research has a quantitative approach where the European Framework of Digital Competencies for Citizenship was used as a reference to identify and establish the level of the competencies that were evaluated. The IKANOS Test was used as a data collection tool. The results show that students know the importance of keeping their devices safe and how valuable the information found on them is. On the other hand, the results also show a considerable percentage of students who do not have the knowledge and are at a basic level of knowledge to solve technical problems with their devices.
基金supported by SW Copyright Ecosystem R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports,and Tourism in 2023(No.RS-2023-00224818).
文摘Open-source licenses can promote the development of machine learning by allowing others to access,modify,and redistribute the training dataset.However,not all open-source licenses may be appropriate for data sharing,as some may not provide adequate protections for sensitive or personal information such as social network data.Additionally,some data may be subject to legal or regulatory restrictions that limit its sharing,regardless of the licensing model used.Hence,obtaining large amounts of labeled data can be difficult,time-consuming,or expensive in many real-world scenarios.Few-shot graph classification,as one application of meta-learning in supervised graph learning,aims to classify unseen graph types by only using a small amount of labeled data.However,the current graph neural network methods lack full usage of graph structures on molecular graphs and social network datasets.Since structural features are known to correlate with molecular properties in chemistry,structure information tends to be ignored with sufficient property information provided.Nevertheless,the common binary classification task of chemical compounds is unsuitable in the few-shot setting requiring novel labels.Hence,this paper focuses on the graph classification tasks of a social network,whose complex topology has an uncertain relationship with its nodes'attributes.With two multi-class graph datasets with large node-attribute dimensions constructed to facilitate the research,we propose a novel learning framework that integrates both meta-learning and contrastive learning to enhance the utilization of graph topological information.Extensive experiments demonstrate the competitive performance of our framework respective to other state-of-the-art methods.
基金supported by the Defense Industrial Technology Development Program,China(JCKY2021208B036).
文摘With the wide application of the Internet of Things(IoT),storing large amounts of IoT data and protecting data privacy has become a meaningful issue.In general,the access control mechanism is used to prevent illegal users from accessing private data.However,traditional data access control schemes face some non-ignorable problems,such as only supporting coarse-grained access control,the risk of centralization,and high trust issues.In this paper,an attribute-based data access control scheme using blockchain technology is proposed.To address these problems,attribute-based encryption(ABE)has become a promising solution for encrypted data access control.Firstly,we utilize blockchain technology to construct a decentralized access control scheme,which can grant data access with transparency and traceability.Furthermore,our scheme also guarantees the privacy of policies and attributes on the blockchain network.Secondly,we optimize an ABE scheme,which makes the size of system parameters smaller and improves the efficiency of algorithms.These optimizations enable our proposed scheme supports large attribute universe requirements in IoT environments.Thirdly,to prohibit attribute impersonation and attribute replay attacks,we design a challenge-response mechanism to verify the ownership of attributes.Finally,we evaluate the security and performance of the scheme.And comparisons with other related schemes show the advantages of our proposed scheme.Compared to existing schemes,our scheme has more comprehensive advantages,such as supporting a large universe,full security,expressive policy,and policy hiding.
文摘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.
