Personal Data Privacy Protection Research Articles

International Law Protection of Cross-Border Transfers of Personal Information Based on Cloud Computing and Big Data

Cross-border data is crucial for the digital economy s growth, but uncontrolled transfers risk national security, public interests, corporate interests, and data sovereignty. Currently, cross-border data regulation lacks unified global rules, with existing ones dominated by Europe and the U.S. China needs to improve its international legal protection for cross-border personal information transfer. This paper examines China s mechanisms for personal data privacy protection in digital trade, including frameworks for big data, cloud computing, and cloud service selection for data lifecycle privacy protection. It suggests clarifying data controllers and exporters obligations, strengthening regulatory responsibilities, improving cross-border data legislation, enhancing law operability, and advocating for stronger international cooperation and participation in global rule-making to protect personal privacy information rights.

AI-driven anonymization: Protecting personal data privacy while leveraging machine learning

AbstractThe development of artificial intelligence has significantly transformed people's lives. However, it has also posed a significant threat to privacy and security, with numerous instances of personal information being exposed online and reports of criminal attacks and theft. Consequently, the need to achieve intelligent protection of personal information through machine learning algorithms has become a paramount concern. Artificial intelligence leverages advanced algorithms and technologies to effectively encrypt and anonymize personal data, enabling valuable data analysis and utilization while safeguarding privacy. This paper focuses on personal data privacy protection and the promotion of anonymity as its core research objectives. It achieves personal data privacy protection and detection through the use of machine learning's differential privacy protection algorithm. The paper also addresses existing challenges in machine learning related to privacy and personal data protection, offers improvement suggestions, and analyzes factors impacting datasets to enable timely personal data privacy detection and protection.

Collusion resistant multi-authority access control scheme with privacy protection for personal health records

The personal health record (PHR) is an important part of the smart health system. The medical profession can provide medical services to patients in time by accessing PHRs stored in the cloud. However, PHR suffers from unauthorized access and disclosure of a large amount of private information at present. Privacy protection of personal data and tracking and accountability of malicious users have been important issues in smart health systems. In this paper, we construct an accountable access control scheme based on ciphertext-policy attribute-based encryption (CP-ABE) scheme. Blockchain accounting node distributes the decryption key, and an audit mechanism judges whether the key is leaked by a malicious user or a corrupt node. The authority and user jointly generate the complete decryption key, which reduces the power of authority and resists collusion attacks between revoked users and unrevoked users. The revocation mechanism deletes the malicious users by updating parts of the ciphertext. Furthermore, blockchain records all operations in the form of transactions that can prevent tampering with ciphertext and trace the dishonest behavior of each entity. Security analysis and performance efficiency comparison show that our scheme is more practical and has important practical significance than the existing schemes.

PDPChain: A Consortium Blockchain-Based Privacy Protection Scheme for Personal Data

With the advances and innovations in digital technologies, blockchain has empowered advancements in communications and networking, promising to build trust and establish secure decentralized communications networks. Unfortunately, current personal data privacy protection schemes still suffer from explicit storage, lack of data ownership and implementation of fine-grained access control by users, and lack of transparency and auditability of data. In this article, we propose a personal data privacy protection scheme based on consortium blockchain that stores original data encrypted with an improved Paillier homomorphic encryption mechanism, namely PDPChain, where users realize fine-grained access control based on ciphertext policy attribute-based encryption (CP-ABE) on blockchain. In this scheme, consortium blockchain combines distributed private clusters to store the encrypted data, improving data transmission efficiency, and guaranteeing user privacy and security through off-chain storage and on-chain transmission synergy. In addition, it is more lightweight encryption and demarcation, ultimately protecting personal data privacy and providing a secure and trusted way to obtain information for data mining. For the performance testing, data in the form of files are used as an example, and the scheme is designed and simulated on Hyperledger Fabric and InterPlanetary File System. Experimental results show that the improved Paillier encryption mechanism reduces the overall encryption and decryption elapsed time by 25% and encryption elapsed time by 48%. Furthermore, the proposed CP-ABE access control method is adaptive to storing and sharing a massive amount of data. With the increase in the number of access control policies, the overall time-consuming of the scheme does not increase, and the time-consuming of decryption can also be stabilized at about 2 s.

DICON: A Domain-Independent Consent Management for Personal Data Protection

The development of technology accelerated the digital transformation of information systems. As a consequence of this digitization, data became available at any time and in any place. However, despite this ease of data accessibility, persons’ privacy concerns and threats to data privacy have emerged. Thus, serious privacy problems arise while collecting, storing, accessing, sharing, and archiving personal data. Consent management aims to prevent these problems by preserving privacy and protecting personal data. Hence, there are international treaties and legal regulations for personal data protection which state that consent is required to collect, store, manage and share personal data. In this study, a Semantic Web-based personal consent management model is proposed to protect personal data privacy. The proposed model is domain-independent and aims to control and manage the consent of a person. In order to provide the privacy protection of personal data, the proposed model allows individuals to establish their privacy preferences by determining who can access their personal information, for what purposes, and under what circumstances. For this purpose, a group of ontology is created to ensure the informed consent process. The proposed consent management model is generic. As similar to general personal information, personal health information is also sensitive and must be protected from data leakage. Therefore, the proposed generic model is implemented with Semantic Web technologies and demonstrated for the healthcare domain.

An information source localization algorithm based on cellular automata model

In order to research the localization problem of information source in a social network, this paper first constructs a complex network node model (Random-Susceptible-Infected-Recovered, R-SIR) based on cellular automata theory, and then utilizes this model to calculate the number of forwarding paths from a node to other forwarding nodes and the probability of occurrence of such forwarding paths, and presents an information source search method based on node reachability measure to locate the information distribution source in the information forwarding network. Theoretical analysis indicates that the algorithm, on the one hand, can avoid the time complexity problem brought by the maximum likelihood estimation, and on the other hand, overcome the inaccuracy problem of step number estimation in accessibility measure. Experimental results show that the algorithm has higher recognition rate and recognition accuracy for the information source in a multi-node network, and can search the information source, and is of great significance for the privacy protection of personal data.

Personal Data Privacy Protection: A Review on Malaysia’s Cyber Security Policies

Personal data privacy is a crucial aspect of information management. Strategic information management must consider the data security and must take all the necessary practices to ensure personal data protected in a closed or open network environment. Data privacy legislation has been formulated by the government to ensure the ethics of handling sensitive information such as personal, banking and another aspect of information like health and medical information must be according to law stipulated by the acts. Malaysia as a growing electronic government provider in the region has adopted several legislation to ensure the enforcement of the data protection achieved the international standard to ensure global recognition especially by international business in enhancing foreign direct investment (FDI) and to protect local enterprises.

Inside the Mind's Eye: An International Perspective on Data Privacy Law in the Age of Brain-Machine Interfaces

Brain machine interface (BMI) technologies extract activity and translate that activity into commands that control external devices. BMIs have been used by patients with physical disabilities to allow for thought-controlled use of computers, prosthetic limbs, and robotic arms. In the commercial context, recreational BMIs are joining the internet of things through applications in video gaming and beyond. BMI systems collect and interpret large amounts of data obtained directly from the brain. This brain data' contains highly intimate information about individual level intentions, cognition, and states of health and disease. Public and private sector entities will have access and control over data, raising concerns over personal data privacy and protection. This paper examines personal data privacy regimes in relation to data across three jurisdictions: Canada, the United States, and the European Union. In relation to the governance of personal data in the private sector, the United States adopts a self-regulatory mechanism, while the European Union assumes a rights-based approach, with Canada's position being intermediate to the two extremes. All three jurisdictions seem to fall short when considering data privacy. The narrow coverage of the United States privacy legislation will exclude most data, while aspects of the EU's rights-based framework will overcompensate and hamper fruitful implementations of BMI technology. Canada's middle-ground approach seems to leave certain gaps in protection for data. Pertaining to government agencies, all three jurisdictions set a low threshold, providing for unfettered governmental access in the name of law enforcement and national security interests. The paper concludes with policy recommendations that recognize the need for stronger personal data privacy protection in the age of BMIs, while balancing the need to foster continued innovation in the BMI technology space. Note: In press at: Albany Law Journal of Science and Technology.

Data Privacy Protection: A Study on Students Awareness of Personal Data Privacy Protection in an E-Health Environment

Data Privacy Protection: A Study on Students Awareness of Personal Data Privacy Protection in an E-Health Environment

La protección de datos personales en la jurisprudencia del Tribunal Constitucional de Chile

This paper systematize and critically analyzes the jurisprudence of the Constitutional Court of Chile on the protection of personal data. To fulfill that purpose, order chronologically its cases in two stages: the first, in which assumes no constitutional significance of the protection of personal data and, second, in which it does. The analysis starts with a brief reference to the foundation that gives genuinely constitutional relevance to this topic. To the author, it will be in two points: in its demand from the perspective of fundamental rights and, moreover, as a necessity for the limitation of the state power. The first stage is characterized by a jurisprudential development of typical problems on the protection of personal data, without making a separate analysis of the constitutional guarantee of privacy protection. The second, meanwhile, will be characterized by distinctly analyze the problematic of the protection of personal data privacy protection, in the context of his constitutional basis. Finally, it summarize conclusions derived from analysis.

Collaborative business and data privacy: Toward a cyber-control?

The pervasive use of communication and information technologies can be seen as a driving force to develop distributed organisations. As far as collaborative business is concerned, short-term e-collaboration can be developed. In this case, security policies must be integrated in the common business process (BP) organisation so that trusted co-operation can be established. For this purpose we propose to couple a generic authorisation workflow to the business process own workflow and to report all the actions on the shared information systems. Nevertheless, reporting tools can be seen as threats on data privacy. We propose here a multi-level architecture to provide both a sufficient security level and a personal data privacy protection.

Self‐Regulatory Trajectories in the Shadow of Public Power: Resolving Digital Dilemmas in Europe and the United States

Although industry self‐regulation has developed into a preferred regulatory strategy for the digital economy, self‐regulatory solutions adopted in the U.S. and the European Union differ considerably. We argue that variation in the shadow of public power—the public sector tools employed to induce industry collective action—sets the two on distinct self‐regulatory trajectories. Legalistic self‐regulation dominates in the U.S. and coordinated self‐regulation in Europe. Expectations derived from the model are evaluated in case studies of online content regulation and personal data privacy protection.

Personal data privacy protection in an age of globalization: the US-EU safe harbor compromise

Hypotheses on the impact of global trade and information flows on state sovereignty and interstate policy convergence have proliferated ahead of empirical studies assessing their explanatory accuracy or predictive powers. The recent personal data privacy protection dispute between the European Union and the United States is a critical case study for examining and refining many of these hypotheses. Personal data privacy protection is a policy realm at once at the forefront of trade and information technology and one that implicates fundamental cultural beliefs and domestic practices. Thus, it is a dispute where the forces allegedly driving and resisting 'globalization' are present. This paper will analyze the 1998 EU Data Protection Directive and the 'safe harbor' compromise agreement between the European Union and the United States to advance the debate over the impact of globalization on state sovereignty, the respective importance and interplay of external and internal forces in shaping state policy, and whether and when the forces of globalization contribute to a reduction in regulation (a 'race-to-the-bottom') or to a higher level of protective regulation ('a race-to-the-top').