Protection Of Individual Privacy Research Articles

Personal Health Data Anonymization in China: The Legal Framework and Its Refinements

China has adopted a dual approach to managing the anonymization of personal health data. In the context of the increasing collection of personal health information, china's current legal standards and practices of data anonymization are inadequate for data protection and utilization. In particular, the requirements of “unidentifiable” and “irreversible” for anonymized data are too high to achieve, and cannot properly address the challenges posed by technological progress. There are also inherent conflicts between the standards of “unidentifiable” and “irreversible” with the guidelines of “de-identification”. These issues affect the effective use of health data and the protection of individual privacy in China. Through a comparative analysis of the EU's “risk management” approach and the US's “de-identification” method in data anonymization, this paper explores the possibility of optimizing the “risk management” approach and the “de-identification” operation to reduce the regulatory uncertainty while enhancing data anonymization protection. The paper proposes suggestions for revising and improving the requirements for personal health data anonymization in China. To better adapt to technological changes and the diverse needs for information use, this paper argues for the establishment of a dynamic legal framework that protects privacy and promotes data utilization, as well as draws on international practices to provide more flexible and adaptable legal responses.

Does the type of privacy-protective behaviour matter? An analysis of online privacy protective action and motivation

ABSTRACT Individual decisions to protect privacy online are complex. Most research has treated privacy protection measures as a single, unitary category. This paper challenges this notion by looking at multiple categories of privacy protection behaviour. Our data are from the Oxford Internet Survey (OxIS) as well as insights from the semi-structured interviews. We find two types of privacy protective actions, which we call security actions and preventive actions. We use logistic regression, based on protection motivation theory, to investigate what leads people to take either type of action. The contribution of this research is three-fold. First, it shows that individual privacy protection cannot be treated as a single category. Second, it demonstrates that there are different motivations underlying different privacy protection actions. Finally, it proposes an expansion of protection motivation theory to include a contextual aspect based on Nissenbaum (2009, Privacy in context. Stanford University Press). This expansion considers the privacy context defined by actor, activity, values and norms. We suggest that future work on privacy should consider multiple categories and contextual settings.

أخلاقيات الذكاء الاصطناعي: بحث في المعضلات الأخلاقية المرتبطة بالذكاء الإصطناعي

The significance of ethics in artificial intelligence (AI) cannot be overstated, as it encompasses the foundational principles guiding the responsible creation, deployment, and management of AI technologies. As AI systems increasingly permeate every facet of our lives—from healthcare and education to security and entertainment—their decisions and actions have profound implications not only on individual rights and privacy but also on societal norms and values. Ethical considerations in AI are paramount to ensure that these technologies enhance human well-being, uphold fairness, and protect freedoms, rather than perpetuate biases, exacerbate inequalities, or undermine democratic institutions. The importance of AI ethics lies in its ability to provide a framework for navigating the complex moral dilemmas presented by AI, such as the balance between innovation and regulation, the protection of individual privacy versus the benefits of big data, and the prevention of AI misuse. By foregrounding ethical principles, stakeholders—including developers, policymakers, and users—can work towards the development of AI technologies that are not only technologically advanced but also socially responsible and aligned with human values. This emphasis on ethics ensures that as AI systems become more autonomous and integral to our daily lives, they do so in a manner that is transparent, accountable, and inclusive, thereby fostering trust and confidence in their widespread adoption and use.

Privacy Protection for Marginal-Sensitive Community Individuals Against Adversarial Community Detection Attacks

Social networks especially the social communities facilitate the rapid and rich social activities of all individuals in the world. However, advanced community detection brings serious privacy disclosure (e.g., whether two important and sensitive individuals belong to the same community?) to us. For instance, online plainclothes policemen can be regarded as marginal community users who are often in the same community initially and in need of penetrating into as many as possible different communities to collect illegal evidence of network criminals, but adversarial community inference which can maliciously disclose the sensitive user relationships within a target community will expose their privacy and lead to task failure. Thus, privacy protection for the marginal community users becomes an urgent issue which is still open so far. In this work, we aim to study the community privacy protection for target marginal individuals of a community against multiple adversarial community detection (ACD) attacks. First, we define the marginal community user hiding problem and propose a marginal user pair selection strategy. Second, to enhance the privacy effectiveness of conventional methods, we propose a deep graph learning approach to maximally find the minimum link perturbation cost. Finally, we conduct various community detection attacks on many real social graphs, and the experimental results show that our method can more effectively hide the marginal-sensitive user pairs than baselines.

Privacy-Aware and AI Techniques for Healthcare Based on K-Anonymity Model in Internet of Things

The government and industry have given the recent development of the Internet of Things in the healthcare sector significant respect. Health service providers retain data gathered from many sources and are useful for patient diagnostics and research for pivotal analysis. However, sensitive personal information about a person is contained in healthcare data, which must be protected. Individual privacy protection is a crucial concern for both people and organizations, particularly when those firms must send user data to data centers due to data mining. This article investigated two general states of increasing entropy by changing the entropy of the class set of characteristics based on artificial intelligence and the k-anonymity model in privacy in context, and also three different strategies have been investigated, i.e., the strategy of “selecting the feature with the lowest number of distinct values,” “selecting the feature with the lowest entropy,” and “selecting the feature with the highest entropy.” For future tasks, we can find an optimal strategy that can help us to achieve optimal entropy in the least possible repetition. The results of our work have been compared by lightweight and MH-Internet of Things, FRUIT methods and shown that the proposed method has high efficiency in entropy criteria.

An effective iris biometric privacy protection scheme with renewability

Iris is considered a unique, robust biometric that can be used as an authentication factor. Privacy protection of individuals’ iris templates is an essential requirement. The essential attributes needed to ensure privacy of biometrics are irreversibility, renewability, and unlinkability. Information distortion is an effective way to achieve the privacy. However, this may lead to some degradation of recognition performance. A trade-off between recognition performance and the privacy level provided by the authentication system arises when it tries to provide both simultaneously. This paper proposes a novel approach that ensures the privacy of each individual's iris by distorting iris feature information. At the meantime, this approach balances the trade-off mentioned above up to a tolerable level. The binary feature templates of iris biometrics are transformed into another irreversible binary representation by manipulating them block-wise. The results obtained against two datasets using proposed approach illustrate excessive Genuine Acceptance Rates (GAR) when the approach accepts imposters at a rate which is reduced to 0.01 %. The proposed approach satisfies the three attributes that a biometric system should fulfil, namely irreversibility, renewability, and unlinkability. The renewability attribute induces the cancellability of iris biometric template. Compared to the existing works, the proposed approach maintains Equal Error Rate (EER) at 0.18 %.

Human Rights in the Era of Surveillance: Balancing Security and Privacy Concerns

In today's rapidly evolving digital landscape, the delicate equilibrium between security imperatives and the protection of individual privacy is under intense scrutiny. This abstract explores the multifaceted challenges posed by the proliferation of surveillance technologies and their impact on human rights. As governments and corporations harness the power of surveillance for security purposes, concerns over privacy violations, data breaches, and potential abuses of power have come to the forefront. This abstract delves into the evolving legal and ethical frameworks governing surveillance practices, highlighting key debates and global perspectives. It also examines the implications for fundamental human rights such as freedom of expression, privacy, and assembly. By examining case studies, emerging technologies, and international efforts to strike a balance between security and privacy, this abstract seeks to shed light on the complex dynamics of this contemporary issue and proposes potential avenues for reconciling these often conflicting interests. Ultimately, it calls for a thoughtful and inclusive dialogue that can guide the development of responsible surveillance policies that respect individual rights and uphold the principles of a free and democratic society.

Individual Privacy and Data Protection Rights. The Legal and Economic Consequences of the Violation During the Covid-19 Pandemic

The basic constitutional freedoms and rights of a person and citizen are in principle unlimited: the full scope of their exercise is the rule, and the restriction determined by law can only be an exception based on explicit constitutional authority and the legitimate aim of the restriction determined by the Constitution. That being so, the restrictions - in addition to being based on constitutional authority and pursuing constitutional objectives - should be commensurate with the needs to achieve these objectives. This means that restrictive legal rules must be suitable for achieving the legitimate aim pursued, must not be stricter than necessary and must be balanced between the constitutionally guaranteed subjective right of the individual and the interests of society.In this paper, the authors point out the economic and legal consequences of the violation of individual privacy and data protection rights caused by the public disclosure of personal data of people who, at a certain time, were obliged to self-isolate due to suspicion of Covid-19 virus infection. 

Research on Users’ Privacy-Sharing Intentions in the Health Data Tracking System Providing Personalized Services and Public Services

The utilization of user privacy data in the health data tracking system (HDTS) offers numerous benefits for businesses and public services, contingent upon users’ privacy sharing intentions. However, previous research neglected users’ preferences for public services and focused mainly on personalized services. Additionally, traditional privacy calculus theory has a limited focus on data security, leaving gaps in understanding individual and societal aspects. This study aims to fill these gaps by examining the influence of risk perception and factors like potential loss expectations, perceived personalized service benefits, group value identification, perceived public service utility, and perceived privacy on privacy sharing intentions in the context of personalized and public services. The results indicate a positive relationship between individual privacy protection perception and data sharing intention, as well as a positive relationship between group value identification and perceived public service utility with individuals’ privacy sharing intentions. Moreover, this research uncovers the moderating effect of information type sensitivity on the impact of perceived privacy and perceived public service utility on privacy sharing intentions, while there is no moderating effect of information type sensitivity on the relationship between group value identification and privacy sharing intentions. We recommend improving individual privacy education, ensuring data use transparency, and fostering identification with common group values to increase users’ privacy sharing intentions.

Respecting Autonomy, Privacy, and Information in Maternity Care: A Study of Midwifery Personnel in Mashhad University of Medical Sciences (2022)

Introduction: Health services for mothers and babies should be comprehensive and combined with respect for individual autonomy, privacy, secrets and information of mother and baby. The present study aimed to determine the status of respect for individual autonomy, privacy, secrets and information of mother and baby from the point of view of midwifery personnel working in public hospitals of Mashhad University of Medical Sciences in 2022. Materials & Methods: This cross-sectional descriptive study was conducted by census method and available on 243 midwifery personnel working in Mashhad University of Medical Sciences public hospitals. The data were collected with an electronic researcher's questionnaire and analyzed using SPSS-22 statistical software, one-sample t, independent t, one-way analysis of variance, Tukey's post hoc, and Pearson correlation coefficient at a significance level of p<0.05. Results: The average age of the participants was 35.00±5.02 years (minimum age 24 years and maximum 42 years). The findings showed that the average score of respect for individual autonomy (3.39±0.21), privacy protection (3.64±0.27), and secrets and information of mother and baby (3.71±0.26) is average (p<0.001). Conclusion: Inadequate and weak attitudes of mothers regarding their own rights regarding respect for individual autonomy, privacy, secrets and information can be caused by low self-confidence and lack of mother's knowledge about her and her baby's rights in the hospital. Therefore, managers and health policymakers must strengthen these concepts in health service providers by providing scientific and practical programs.

Turning the moral compass towards transformative research ethics: An inflection point for humanised pedagogy in higher education

Ethical guidance in research is underpinned by the need to show respect for study participants by upholding autonomy in participant decision-making, and confidentiality and protection of individual rights, privacy and interests, yet decision-making could also be influenced by the participant’s sociocultural and belief systems. This calls for a more Africanised approach to research ethics where these values and beliefs are upheld. While national and international ethics guidelines do exist, there is little evidence that such a paradigm shift in research ethics is adequately interrogated in the teaching and learning curricula in higher education, where research skills are developed and nurtured. Critical reflection is used to highlight opportunities to stimulate learning and debates underpinned by humanised pedagogy in higher education.

Consumer Views on Privacy Protections and Sharing of Personal Digital Health Information

Digital health information has many potential health applications, but privacy is a growing concern among consumers and policy makers. Consent alone is increasingly seen as inadequate to safeguard privacy. To determine whether different privacy protections are associated with consumers' willingness to share their digital health information for research, marketing, or clinical uses. This 2020 national survey with an embedded conjoint experiment recruited US adults from a nationally representative sample with oversampling of Black and Hispanic individuals. Willingness to share digital information across 192 different scenarios reflecting the product of 4 possible privacy protections, 3 uses of information, 2 users of information, and 2 sources of digital information was evaluated. Each participant was randomly assigned 9 scenarios. The survey was administrated between July 10 and July 31, 2020, in Spanish and English. Analysis for this study was conducted between May 2021 and July 2022. Participants rated each conjoint profile on a 5-point Likert scale measuring their willingness to share their personal digital information (with 5 indicating the most willingness to share). Results are reported as adjusted mean differences. Of the 6284 potential participants, 3539 (56%) responded to the conjoint scenarios. A total of 1858 participants (53%) were female, 758 (21%) identified as Black, 833 (24%) identified as Hispanic, 1149 (33%) had an annual income less than $50 000, and 1274 (36%) were 60 years or older. Participants were more willing to share health information with the presence of each individual privacy protection, including consent (difference, 0.32; 95% CI, 0.29-0.35; P < .001), followed by data deletion (difference, 0.16; 95% CI, 0.13-0.18; P < .001), oversight (difference, 0.13; 95% CI, 0.10-0.15; P < .001), and transparency of data collected (difference, 0.08; 95% CI, 0.05-0.10; P < .001). The relative importance (importance weight on a 0%-100% scale) was greatest for the purpose of use (29.9%) but when considered collectively, the 4 privacy protections together were the most important (51.5%) factor in the conjoint experiment. When the 4 privacy protections were considered separately, consent was the most important (23.9%). In this survey study of a nationally representative sample of US adults, consumers' willingness to share personal digital health information for health purposes was associated with the presence of specific privacy protections beyond consent alone. Additional protections, including data transparency, oversight, and data deletion may strengthen consumer confidence in sharing their personal digital health information.

ICT applications and the COVID-19 pandemic: Impacts on the individual's digital data, digital privacy, and data protection

A prominent move amid the COVID-19 pandemic is related to the wide use of ICT applications for various reasons. Such services are context-specific and sector-specific, but we see transformative directions leading to digital data collection, monitory, and management platforms. For now, these have been beneficial to evaluate trends and issues related to the pandemic. Some aggregated data could also help decision-making processes, which are helpful to contain such disruptive events. However, the main concern is the use of the individual's data and information, which means we may shift to micro-management and eventual controlling tools that could harm data protection processes. Undoubtedly, the use of ICTs during the COVID-19 pandemic has been primarily positive at multiple scales, but we have to evaluate the pros and cons before accepting all data collection processes. Such ICT-mediated platforms and/or applications must remain beneficial to all and avoid breaching the individual's data protection. This short communication paper first introduces ICT applications during the COVID-19 before providing an overview and further analysis of the situation of the ICT applications. Afterward, it discusses issues of data privacy, data collection, and data use, which are the backbone of ICT applications. The discussions highlight that future research in this area could look into associated issues related to digital privacy, data-driven approaches, and data protection regulations.

Video nadzor i veštačka inteligencija - uticaj na pravo na privatnost i prava intelektualne svojine

The protection of individual privacy is increasingly questioned in the context of today's high-tech video surveillance by using superior technologies, such as Artificial Intelligence (smart cameras and video surveillance systems, biometrics and facial recognition) which is capable of analyzing a huge amount of data, identifying links between that data and de-anonymizing them. Technology is synonymous with evolution. Yet, the advantages of using new technology are combined with great risks. The use of video cameras for surveillance raises important privacy issues. Biometric remote identification can only be performed under certain safeguards, in the context of a justified interest and with respect for the principle of proportionality. In recent years, facial recognition technology has become increasingly widespread, and highly controversial, as it is omnipresent (at airport check-in lines, police departments, pharmacies, etc.). While it may add a sense of security and comfort for businesses implementing it, such technology has been widely criticized by privacy advocates, especially for its built-in racial bias and potential for abusive use. "Real-time" biometric identification of individuals in public-accessible spaces for law enforcement purposes is seen as highly intrusive on the rights and freedoms of the individuals concerned, but it also evokes a sense of constant surveillance and indirectly discourages the exercise of freedom of assembly and other fundamental rights. Another important issue is the use of video surveillance in a context where it can cause problems in terms of intellectual property. The unauthorized use of recordings may affect confidential business operations. Trade secrets and confidential information are often an essential part of a company's intellectual property portfolio. Thus, companies have to take extra steps to ensure that images captured by cameras are stored securely and accessible only to authorized personnel. Cameras can capture images of artistic works, performances, exhibitions, and uncontrolled access can lead to infringement of copyright and related rights. As captured videos can be uploaded to platforms, such use of intellectual property can lead to copyright infringement, especially when the video entails the use of copyrighted material without the owner's permission, which constitutes an infringement of intellectual property rights.

A Dynamic Multi-Attribute Resource Bidding Mechanism with Privacy Protection in Edge Computing

In edge computing, a reasonable edge resource bidding mechanism can enable edge providers and users to obtain benefits in a relatively fair fashion. To maximize such benefits, this paper proposes a dynamic multi-attribute resource bidding mechanism (DMRBM). Most of the previous work mainly relies on a third-party agent to exchange information to gain optimal benefits. It is worth noting that when edge providers and users trade with third-party agents which are not entirely reliable and trustworthy, their sensitive information is prone to be leaked. Moreover, the privacy protection of edge providers and users must be considered in the dynamic pricing/transaction process, which is also very challenging. Therefore, this paper first adopts a privacy protection algorithm to prevent sensitive information from leakage. On the premise that the sensitive data of both edge providers and users are protected, the prices of providers fluctuate within a certain range. Then, users can choose appropriate edge providers by the price-performance ratio (PPR) standard and the reward of lower price (LPR) standard according to their demands. The two standards can be evolved by two evaluation functions. Furthermore, this paper employs an approximate computing method to get an approximate solution of DMRBM in polynomial time. Specifically, this paper models the bidding process as a non-cooperative game and obtains the approximate optimal solution based on two standards according to the game theory. Through the extensive experiments, this paper demonstrates that the DMRBM satisfies the individual rationality, budget balance, and privacy protection and it can also increase the task offloading rate and the system benefits.

Understanding Risks of Privacy Theater with Differential Privacy

Differential privacy is one of the most popular technologies in the growing area of privacy-conscious data analytics. But differential privacy, along with other privacy-enhancing technologies, may enable privacy theater. In implementations of differential privacy, certain algorithm parameters control the tradeoff between privacy protection for individuals and utility for the data collector; thus, data collectors who do not provide transparency into these parameters may obscure the limited protection offered by their implementation. Through large-scale online surveys, we investigate whether explanations of differential privacy that hide important information about algorithm parameters persuade users to share more browser history data. Surprisingly, we find that the explanations have little effect on individuals' willingness to share data. In fact, most people make up their minds about whether to share before they even learn about the privacy protection.

COVID-19 contact tracing apps and the governance of collective action: social nudges, deliberation, and solidarity in Europe and beyond

ABSTRACT During the COVID-19 pandemic, Digital Contact Tracing (DCT) tools were deployed by governments in Europe and beyond as a novel mobile technology to assist traditional manual contact tracing to track individuals who have come in close contact with an infected person. The public debate on this topic focused strongly on the protection of individual privacy. While this debate is important, it fails to address important governance questions – such as, for example, that DCT tools took on the role of social nudges, namely, tools of soft regulation that calibrate information flows so as to “push” people to act in ways that promote collective purposes. Social nudges include a range of norms and values that, however, are built into the technological and social features of the nudge, rather than rendering them open to public scrutiny and debate. Although the use of contact tracing apps is being phased out, the digitization of contact tracing can be seen as a case study of the broader trend towards digitization of the provision of health services. Debates of their governance thus have broader implications for the governance of data driven tools deployed for public health purposes in times of crisis.

Integrated but Isolated: Implications from a Systematic Review of the Access Control Ecosystem for Individual Participant Data in Clinical Studies

AbstractWhile the importance of open science is further highlighted during the pandemic, the challenges of managing and sharing individual participant data (IPD) derived from clinical studies never cease. The nature of IPD, e.g., confidentiality or sensitivity, makes it difficult to maintain a good balance between data sharing and individual privacy protection. To date, many access control mechanisms for IPD do exist, but conventional solutions and services are deemed scattered and still not in place. To gain a more comprehensive understanding of the IPD sharing tensions, we conducted a systematic literature review with 64 academic publications that discuss the access control mechanisms built for IPD in clinical studies. Via the knowledge infrastructure (KI) framework, we identified nine key aspects involved and the relationships between major stakeholders in the IPD access control ecosystem. Our results anticipate informing the future design of an IPD management checklist that data professionals can use to guide their clients when releasing sensitive biomedical data.

Compliance Is Doable! A Framework for Navigating Privacy Regulations in Public Health and Public Safety Partnerships.

The past decade witnessed an explosion of public health/public safety collaborations. Many emerged as pragmatic responses to the opioid epidemic, where communities struggled to help individuals at risk of fatal and nonfatal overdoses. Multidisciplinary programs formed to actively engage people with services, including harm reduction, treatment, and peer support, instead of arrest. These initiatives blur traditional lines between public safety, health, treatment, and services. Novel applications of HIPAA and 42 CFR Part 2 created confusion, sometimes discouraging new ways of doing business and other times leading to a disregard for individual privacy protections in the interest of “doing the right thing.” Neither is ideal. In this article, the authors present a framework for collaborations to navigate issues related to privacy, review relevant laws, provide a practical application to public health/public safety partnerships, and offer practice pointers. With this resource, stakeholders are empowered to create effective and compliant overdose response programs.

Towards Personalized Privacy-Preserving Truth Discovery Over Crowdsourced Data Streams

Truth discovery is an effective paradigm which could reveal the truth from crowdsouced data with conflicts, enabling data-driven decision-making systems to make quick and smart decisions. The increasing privacy concern promotes users to perturb or encrypt their private data before outsourcing, which poses significant challenges for truth discovery. Although several privacy-preserving truth discovery mechanisms have been proposed, none of them take personal privacy expectation into consideration. In this work, we propose a novel personalized privacy-preserving truth discovery (PPPTD) framework over crowdsourced data streams to achieve timely and accurate truth discovery while guaranteeing the protection of individual privacy. The key challenges of PPPTD lie in improving the accuracy of truth estimation from the perturbed streaming data with personalized protection level. To address these challenges, we first develop a personalized budget initialization mechanism to quantify each user’s privacy protection requirement, and allocate personalized privacy budgets to users according to their privacy requirements. Then we propose a deviation-aware weighted aggregation method to improve the accuracy of truth discovery from streaming data with varying degrees of perturbation. In order to achieve privacy-utility tradeoff, we further propose an influence-aware adaptive budget adjustment mechanism that adaptively re-allocates privacy budgets to users based on the evolution of their influence in the weighted aggregation. We prove that PPPTD can achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -differential privacy over the whole data generated by users and satisfy individual personalized privacy requirements. Extensive experiments on two real-world datasets demonstrate the effectiveness of PPPTD.