Disclosure Risk Research Articles

A blind signature scheme for IoV based on 2D-SCML image encryption and lattice cipher

Today’s Internet of Vehicles (IoV) faces many security risks in the data transmission process, and image data is more vulnerable to security threats in the transmission process due to its special characteristics such as large amounts of information and high visibility. Therefore, to guarantee the dependability of data transmission in the IoV environment, this paper designs a blind signature scheme for IoV based on two-dimensional sine cosine cross-chaotic mapping (2D-SCML) image encryption and lattice cipher (BSS-IoV). The innovation of this scheme is that it aims at blind signature of image information, blinds it before sending the information, and combines the lattice public key encryption algorithm to better ensure the safe and reliable transmission of information and reduce the risk of information disclosure. To further ensure the security of the scheme, an image encryption algorithm based on 2D-SCML and pixel splitting (2PS-IEA) is proposed, which is used to blind the information and thus reduce the risk of information leakage on the one hand, on the other hand, it is used in the signature process to ensure the security of the signed information. The 2D-SCML is derived from the cross-model structure proposed in this paper. Through simulation results and experimental analysis, the values of NPCR and UACI, respectively, 99.6094% and 33.4635%, are close to ideal values. And 50% of the cut image can also recover the rough information, which indicates that the signature scheme has the security against differential attacks, cut attacks and noise attacks. Moreover, the security analysis shows that the scheme has the anti-tamper, anti-repudiation and traceability.

DPIVE: A Regionalized Location Obfuscation Scheme with Personalized Privacy Levels

The popularity of cyber-physical systems is fueling the rapid growth of location-based services. This poses the risk of location privacy disclosure. Effective privacy preservation is foremost for various mobile applications. Recently, geo-indistinguishability and expected inference error are proposed for limiting location leakages. In this article, we argue that personalization means regionalization for geo-indistinguishability, and we propose a regionalized location obfuscation mechanism called DPIVE with personalized utility sensitivities. This substantially corrects the differential and distortion privacy problem of the PIVE framework proposed by Yu et al. on NDSS 2017. We develop DPIVE with two phases. In Phase I, we determine disjoint sets by partitioning all possible positions such that different locations in the same set share the Protection Location Set (PLS). In Phase II, we construct a probability distribution matrix in which the rows corresponding to the same PLS have their own sensitivity of utility (PLS diameter). Moreover, by designing a QK-means algorithm for more search space in 2-D space, we improve DPIVE with a refined location partition and present fine-grained personalization, enabling each location to have its own privacy level endowed with a customized privacy budget. Experiments with two public datasets demonstrate that our mechanisms have the superior performance, typically on skewed locations.

Case study on communicating with research ethics committees about minimizing risk through software: an application for record linkage in secondary data analysis.

In retrospective secondary data analysis studies, researchers often seek waiver of consent from institutional Review Boards (IRB) and minimize risk by utilizing complex software. Yet, little is known about the perspectives of IRB experts on these approaches. To facilitate effective communication about risk mitigation strategies using software, we conducted two studies with IRB experts to co-create appropriate language when describing a software to IRBs. We conducted structured focus groups with IRB experts to solicit ideas on questions regarding benefits, risks, and informational needs. Based on these results, we developed a template IRB application and template responses for a generic study using privacy-enhancing software. We then conducted a three-round Delphi study to refine the template IRB application and the template responses based on expert panel feedback. To facilitate participants' deliberation, we shared the revisions and a summary of participants' feedback during each Delphi round. 11 experts in two focus groups generated 13 ideas on risks, benefits, and informational needs. 17 experts participated in the Delphi study with 13 completing all rounds. Most agreed that privacy-enhancing software will minimize risk, but regardless all secondary data studies have an inherent risk of unexpected disclosures. The majority (84.6%) noted that subjects in retrospective secondary data studies experience no greater risks than the risks experienced in ordinary life in the modern digital society. Hence, all retrospective data-only studies with no contact with subjects would be minimal risk studies. First, we found fundamental disagreements in how some IRB experts view risks in secondary data research. Such disagreements are consequential because they can affect determination outcomes and might suggest IRBs at different institutions might come to different conclusions regarding similar study protocols. Second, the highest ranked risks and benefits of privacy-enhancing software in our study were societal rather than individual. The highest ranked benefits were facilitating more research and promoting responsible data governance practices. The highest ranked risks were risk of invalid results from systematic user error or erroneous algorithms. These societal considerations are typically more characteristic of public health ethics as opposed to the bioethical approach of research ethics, possibly reflecting the difficulty applying a bioethical approach (eg, informed consent) in secondary data studies. Finally, the development of privacy-enhancing technology for secondary data research depends on effective communication and collaboration between the privacy experts and technology developers. Privacy is a complex issue that requires a holistic approach that is best addressed through privacy-by-design principles. Privacy expert participation is important yet often neglected in this design process. This study suggests best practice strategies for engaging the privacy community through co-developing companion documents for software through participatory design to facilitate transparency and communication. In this case study, the final template IRB application and responses we released with the open-source software can be easily adapted by researchers to better communicate with their IRB when using the software. This can help increase responsible data governance practices when many software developers are not research ethics experts.

Evaluating the implementation of weekly rifapentine-isoniazid (3HP) for tuberculosis prevention among people living with HIV in Uganda: A qualitative evaluation of the 3HP Options Trial.

Three months of isoniazid-rifapentine (3HP) is being scaled up for tuberculosis (TB) preventive treatment (TPT) among people living with HIV (PLHIV) in high-burden settings. More evidence is needed to identify factors influencing successful 3HP delivery. We conducted a qualitative assessment of 3HP delivery nested within the 3HP Options Trial, which compared three optimized strategies for delivering 3HP: facilitated directly observed therapy (DOT), facilitated self-administered therapy (SAT), and patient choice between facilitated DOT and facilitated SAT at the Mulago HIV/AIDS clinic in Kampala, Uganda. We conducted 72 in-depth interviews among PLHIV purposively selected to investigate factors influencing 3HP acceptance and completion. We conducted ten key informant interviews with healthcare providers (HCPs) involved in 3HP delivery to identify facilitators and barriers at the clinic level. We used post-trial 3HP delivery data to assess sustainability. We used thematic analysis (inductive and deductive) to align the emergent themes with the RE-AIM framework dimensions to report implementation outcomes. Understanding the need for TPT, once-weekly dosing, shorter duration, and perceived 3HP safety enhanced acceptance overall. Treatment monitoring by HCPs and reduced risk of HIV status disclosure enabled DOT acceptance. Dosing autonomy enabled SAT acceptance. Switching between DOT and SAT as needed enabled acceptance of patient choice. Dosing reminders, reimbursement for clinical visits, and social support enabled 3HP completion; pill burden, side effects, and COVID-19-related treatment restrictions hindered completion. All HCPs were trained and participated in 3HP delivery with high fidelity. Training, care integration, prior TPT experience with daily isoniazid, and few 3HP-related serious adverse events enabled adoption, whereas initial concerns about 3HP safety among HCPs, and COVID-19 treatment disruptions delayed 3HP adoption. Refresher training and collaboration among HCPs enabled implementation whereas limited diagnostic facilities for adverse events at the clinic hindered implementation. SAT was modified post-trial; DOT was discontinued due to inadequate ongoing financial support beyond the study period. Facilitated delivery strategies made 3HP treatment convenient for PLHIV and were feasible and implemented with high fidelity by HCPs. However, the costs of 3HP facilitation may limit wider scale-up. Trial registration: ClinicalTrials.gov (NCT03934931); Registered 2nd May 2019; https://clinicaltrials.gov/study/NCT03934931?id = NCT03934931&rank = 1.

Assessing Statistical Disclosure Risk for Differentially Private, Hierarchical Count Data, With Application to the 2020 U. S. Decennial Census

Assessing Statistical Disclosure Risk for Differentially Private, Hierarchical Count Data, With Application to the 2020 U. S. Decennial Census

The Realities of Disclosure Risks in the Age of Dark Patterns and Big Data

The Realities of Disclosure Risks in the Age of Dark Patterns and Big Data

Confidentiality of Personal Data in Case of Cyberthreats

Despite the presence of the current legal regulation of personal data and their circulation on the Internet, Russia has recently become one of the leaders in personal data leaks. It turns out that the right to confidentiality and the right to protection of personal data are not implemented in the digital environment due to the susceptibility to cyberattacks and the lack of appropriate measures and guarantees in this field. The Russian legislator establishes the obligations of the operator of personal data, including the obligation of the operator to notify the authorized body of the leakage of personal data, as well as administrative, criminal and civil liability for their disclosure. However, there are no effective mechanisms that would prevent leaks and other disclosure of personal data in a preventive manner and would allow full compensation for the damage caused to individuals. The legal doctrine proposes to strengthen the penalties for violations in the field of personal data and specify the possibility of compensation for moral damage to a person who has suffered from a leak. The author’s approach is to provide for the need to check the operator’s activities for violations that led to the leakage of personal data (post-control), as well as to introduce a compliance system that allows preventive prevention of leakages and other violations in the field of personal data (a priori control), and train employees to minimize the risks of disclosure and other illegal use of personal data.

Application and risk analysis of brain-computer interface

As society progresses and science and technology develop, brain-computer interface has made eye-catching achievements. Brain-computer interface (BCI) is a technology that realizes information exchange and response by establishing a direct connection between the brain and external devices. Brain-computer interface is one of the main research directions of human-computer interaction, which has great development potential and wide application in the future. However, the risks and challenges beckoned by it, on the other hand, cannot be ignored. It is of great significance to analyze its application and risk and at the same time to develop the solutions. Based on the existing literature, this paper summarized the working principle of the brain-computer interface, combed its application in various modern fields, and explained the current challenges it faces, hoping to provide certain references for the further development of the technology. Pursuant to relevant studies, it was found that the brain-computer interface has wide application prospects in medical treatment, the economy, people's livelihood, education, and the military, and currently, the main challenges cover inaccurate transmission, low safety factors, obvious ethical problems, and privacy disclosure risk.

What should a robot disclose about me? A study about privacy-appropriate behaviors for social robots.

For robots to become integrated into our daily environment, they must be designed to gain sufficient trust of both users and bystanders. This is in particular important for social robots including those that assume the role of a mediator, working towards positively shaping relationships and interactions between individuals. One crucial factor influencing trust is the appropriate handling of personal information. Previous research on privacy has focused on data collection, secure storage, and abstract third-party disclosure risks. However, robot mediators may face situations where the disclosure of private information about one person to another specific person appears necessary. It is not clear if, how, and to what extent robots should share private information between people. This study presents an online investigation into appropriate robotic disclosure strategies. Using a vignette design, participants were presented with written descriptions of situations where a social robot reveals personal information about its owner to support pro-social human-human interaction. Participants were asked to choose the most appropriate robot behaviors, which differed in the level of information disclosure. We aimed to explore the effects of disclosure context, such as the relationship to the other person and the information content. The findings indicate that both the information content and relationship configurations significantly influence the perception of appropriate behavior but are not the sole determinants of disclosure-adequacy perception. The results also suggest that expected benefits of disclosure and individual general privacy attitudes serve as additional influential factors. These insights can inform the design of future mediating robots, enabling them to make more privacy-appropriate decisions which could foster trust and acceptance.

Machine learning models in trusted research environments - understanding operational risks.

Trusted research environments (TREs) provide secure access to very sensitive data for research. All TREs operate manual checks on outputs to ensure there is no residual disclosure risk. Machine learning (ML) models require very large amount of data; if this data is personal, the TRE is a well-established data management solution. However, ML models present novel disclosure risks, in both type and scale. As part of a series on ML disclosure risk in TREs, this article is intended to introduce TRE managers to the conceptual problems and work being done to address them. We demonstrate how ML models present a qualitatively different type of disclosure risk, compared to traditional statistical outputs. These arise from both the nature and the scale of ML modelling. We show that there are a large number of unresolved issues, although there is progress in many areas. We show where areas of uncertainty remain, as well as remedial responses available to TREs. At this stage, disclosure checking of ML models is very much a specialist activity. However, TRE managers need a basic awareness of the potential risk in ML models to enable them to make sensible decisions on using TREs for ML model development.

RESTRICTED DATA MANAGEMENT: THE CURRENT PRACTICE AND THE FUTURE.

Many organizations across the world that manage restricted data have adapted the Five Safes framework (safe data, safe projects, safe people, safe setting, safe output) for their management of restricted and confidential data. While the Five Safes have been well integrated throughout the data life cycle, organizations encounter several challenges with regard to safe data management. In this paper, we review current practices for restricted data management, and discuss challenges and future directions. We focus on data use agreements, disclosure risk review, and training. In the future, organizations managing restricted data may need proactively to take into consideration reducing inequalities in access to scientific data, preventing unethical use of data, and managing various types of data.

People’s political views, perceived social norms, and individualism shape their privacy concerns for and acceptance of pandemic control measures that use individual-level georeferenced data

BackgroundAs the COVID-19 pandemic became a major global health crisis, many COVID-19 control measures that use individual-level georeferenced data (e.g., the locations of people’s residences and activities) have been used in different countries around the world. Because these measures involve some disclosure risk and have the potential for privacy violations, people’s concerns for geoprivacy (locational privacy) have recently heightened as a result, leading to an urgent need to understand and address the geoprivacy issues associated with COVID-19 control measures that use data on people’s private locations.MethodsWe conducted an international cross-sectional survey in six study areas (n = 4260) to examine how people’s political views, perceived social norms, and individualism shape their privacy concerns, perceived social benefits, and acceptance of ten COVID-19 control measures that use individual-level georeferenced data. Multilevel linear regression models were used to examine these effects. We also applied multilevel structure equation models (SEMs) to explore the direct, indirect, and mediating effects among the variables.ResultsWe observed a tradeoff relationship between people’s privacy concerns and the acceptance (and perceived social benefits) of the control measures. People’s perceived social tightness and vertical individualism are positively associated with their acceptance and perceived social benefits of the control measures, while horizontal individualism has a negative association. Further, people with conservative political views and high levels of individualism (both vertical and horizontal) have high levels of privacy concerns.ConclusionsOur results first suggest that people’s privacy concerns significantly affect their perceived social benefits and acceptance of the COVID-19 control measures. Besides, our results also imply that strengthening social norms may increase people’s acceptance and perceived social benefits of the control measures but may not reduce people’s privacy concerns, which could be an obstacle to the implementation of similar control measures during future pandemics. Lastly, people’s privacy concerns tend to increase with their conservatism and individualism.

BEYOND LEGAL FRAMEWORKS AND SECURITY CONTROLS FOR ACCESSING CONFIDENTIAL SURVEY DATA IN THE UNITED STATES: ENGAGING DATA USERS IN DATA PROTECTION.

With growing demand for data reuse and open data within the scientific ecosystem, protecting the confidentialy of survey data and privacy of data subjects is increasingly important. Doing so requires more than legal procedures and technological controls; it requires social and behavioral intervention. In this research note, we delineate the disclosure risks of various types of survey data (e.g., longitudinal data, social network data, sensitive information, biomarkers, and geographic data), the current motivation for data reuse, and challenges to data protection. Despite rigorous efforts to protect data, there are still threats to protection of confidentiality in microdata. Unintentional data breaches, protocol violations, and data misuse are observed even in well-established restricted data access systems, indicating that the systems may all rely heavily on trust. Creating and maintaining that trust is critical to secure data access. We suggest four ways of building trust; User-Centered Design Practices; Promoting Trust for Protecting Confidential Data; General Training in Research Ethics; and Specific Training in Data Security Protocols, with an example of a new project 'Researcher Passport' by the Inter-university Consortium for Political and Social Research. Continuous user-focused improvements in restricted data access systems are necessary so that we promote a culture of trust among the research and data user community, train both in the general topic of responsible research and in the specific requirements of these systems, and offer systematic and holistic solutions.

Kajian Literatur Manajemen Privasi dalam Konteks Hubungan Keluarga di Facebook

The rapid development of technology makes social media an important part of the communication process. Facebook as one of the most popular social media and has many users encourages the emergence of privacy disturbances. As the number of families using Facebook increases, the risk of privacy disclosure also becomes more open in the public sphere. The management of personal data and information on social media has triggered privacy disturbances. The research team used a qualitative methodology with a literature review method to examine privacy management using the theory of Communication Privacy Management (CPM) in the family context on Facebook Social Media. The results of the study show that as owners of individual information, they apply privacy controls through privacy rules and restrictions as a way to decide what to disclose and hide on Facebook. Privacy disclosure decisions are influenced by the quality of family relationships and topics of discussion. Individuals will apply personal boundaries to information related to sex and HIV disease. Privacy controls carried out by individuals in the context of family relationships on Facebook are carried out to avoid turbulence.

TIERED ACCESS TO RESEARCH DATA FOR SECONDARY ANALYSIS.

Research data have expanded in their gradation of the risks associated with both re-identification and harm, which has created a need for multiple levels of access controls beyond public and restricted access. Public-access data have typically been available for download from websites while restricted-access data usually require an application and formal authorization process. The old paradigm of classifying data as public-access or restricted-access is no longer sufficient. Access to research data requires more nuance to ensure the protection of human subjects. In this paper, we describe seven tiers of access to research data. Each tier adds requirements that are necessary to mitigate disclosure risk and confirm appropriate management of the data. Improper handling of the data includes attempting to find a specific individual or household or failing to follow disclosure protection rules for data and output included in papers and presentations. By establishing a ladder of access conditions, each higher tier meets and exceeds the requirements of the lower tiers. While the highest tier meets all requirements, this tier will impede legitimate research for most data. The challenge for repositories is to provide access in a manner that promotes research while specifying security that provides appropriate protections against the risks of re-identification and harm.

Research on Location Privacy Protection Technology in Wireless Sensor Networks Based on Big Data

The digital footprint in wireless sensor networks can bring great academic and commercial value, but it will also bring the risk of privacy disclosure to users. This study discusses the location privacy protection methods in location based service under multiple scenarios. In the experiment, a false location filtering algorithm for real-time location request scenarios and a false path generation model for offline location release scenarios are proposed. The false position filtering algorithm is implemented based on the idea of a large top heap selection query. The algorithm can construct false position candidate sets and filter false positions. The false track generation model combines the false position technology and the generative adversarial networks model, which mainly protects the user’s track data by synthesizing tracks. In the attacker’s recognition experiment of a real location. The minimum distance between the false locations generated by the algorithm proposed in the study is above 400 m and the generation time does not exceed 5 ms, generating a better set of false locations in terms of both effectiveness and efficiency. Compared with several commonly used privacy-preserving algorithms, the proposed algorithm has the lowest probability of being identified with real locations, with no more than 21% overall, and is almost independent of the k value. the recognition accuracy of the trajectory user link task decreases from over 90% to about 34%, indicating that the proposed fake trajectory generation model can effectively protect users’ data privacy. The experimental results demonstrate that the algorithm and model proposed in the study can quickly generate physically dispersed and semantically diverse sets of fake locations and effectively protect users’ trajectory privacy, which is important for users’ digital footprint privacy protection.

Hide Your Distance: Privacy Risks and Protection in Spatial Accessibility Analysis.

Measuring spatial accessibility to healthcare resources and facilities has long been an important problem in public health. For example, during disease outbreaks, sharing spatial accessibility data such as individual travel distances to health facilities is vital to policy making and designing effective interventions. However, sharing these data may raise privacy concerns, as information about individual data contributors (e.g., health status and residential address) may be disclosed. In this work, we investigate those unintended information leakage in spatial accessibility analysis. Specifically, we are interested in understanding whether sharing data for spatial accessibility computations may disclose individual participation (i.e., membership inference) and personal identifiable information (i.e., address inference). Furthermore, we propose two provably private algorithms that mitigate those privacy risks. The evaluation is conducted with real population and healthcare facilities data from Mecklenburg county, NC and Nashville, TN. Compared to state-of-the-art privacy practices, our methods effectively reduce the risks of membership and address disclosure, while providing useful data for spatial accessibility analysis.

Examining the link between distressing life events, social media distress disclosure, and perceived stress: A moderated mediation model

People share not only positive but also distressing personal information on social media. Although much work has been done to understand distress disclosure practices on social media, little is known about the association between social media distress disclosure and different components of disclosure risk, as well as the link between distressing life events, social media distress disclosure, and psychological well-being. This study examines the theoretical antecedents (i.e., situational and non-situational factors) of social media distress disclosure and tests a moderated mediation model that assesses the mediator role of social media distress disclosure on the relationship between distressors (i.e., frequency and intensity of distressing life events) and perceived stress. Results from an online survey (n = 967) revealed that social media users’ distress disclosure frequency is associated with both situational (i.e., distressing life events and exposure to others’ distress disclosure) and non-situational (i.e., perceived risks and benefits of distress disclosure) factors. Specifically, the findings indicated that social media users’ distress disclosure frequency was not only affected by self-oriented risk but also other-oriented risk—imposition risk. Furthermore, the moderated mediation model revealed that the association between distressors and perceived stress was mediated by social media distress disclosure frequency, and this mediated relationship was moderated by risk-benefit calculus and satisfaction with social media distress disclosure. Overall, this study advances our understanding of social media distress disclosure and extends existing self-disclosure theories by specifying the conditions by which individuals can expect an improvement in psychological well-being following disclosure of distressing information on social media.

Antiretroviral Therapy Concealment Behaviors and their Association with Antiretroviral Therapy Adherence among People with HIV: Findings from the Florida Cohort Study.

Little is known about HIV medication concealment behaviors and the effect of medication concealment on antiretroviral therapy (ART) adherence among people with HIV (PWH). This study aims to (1) to describe medication concealment behaviors and factors associated with these behaviors, and (2) assess the association between medication concealment and suboptimal ART adherence. The Florida Cohort Study enrolled adult PWH from community-based clinics around the state from October 2020 to September 2022 (n = 416, 62% aged 50+, 56% male, 44% non-Hispanic Black, 18% Hispanic). Participants responded to questions about sociodemographics, stigma, ART adherence (≥ 85%), symptoms of depression, social networks and disclosure to their networks, and actions to conceal ART to avoid inadvertent disclosure of their HIV status. Analyses were conducted using multivariable logistic regressions models. The most common concealment behavior was hiding ART while having guests over (32%), followed by removing ART labels (26%), and putting ART into a different bottle (16%). Overall, 43% reported ≥ 1 behavior. In multivariable models, depressive symptoms, incomplete disclosure of HIV to close social networks, and not having a close social network were associated with ART concealment. After adjusting for risk factors for suboptimal ART adherence, endorsing hiding medication while having guests was associated with suboptimal ART adherence (aOR 2.87, 95% CI 1.15-7.55). Taking any action and other individual behaviors were not associated. ART concealment behaviors were common but did not consistently negatively influence adherence when accounting for other factors. PWH may want to receive ART medications in ways that ensure privacy and reduce the risk of inadvertent disclosure.

Opportunities to strengthen resilience of health care workers regarding patient safety

BackgroundThe COVID-19 pandemic endangered the quality of health care and the safety of patients and health care workers (HCWs). This provided challenges for HCWs’ resilience and for hospital management and probably increased risks for patient safety incidents (PSI). HCWs may also have experienced psychological consequences as second victims of PSI, but evidence on this is lacking. Therefore, we mapped HCWs’ experiences with PSI during the second wave of COVID-19, the associations of these experiences with the hospital management of patient safety culture and HCWs’ interests in receiving further training.MethodsWe obtained data from 193 HCWs working at the COVID-related departments of one large hospital in eastern Slovakia via a questionnaire developed in direct collaboration with them. We measured PSI experiences as various HCWs’ experiences with near miss and adverse events and the hospital management of patient safety culture using indicators such as risk of recurrence, open disclosure and second victim experiences. For analysis, we used logistic regression models adjusted for age and gender of the HCWs.ResultsOne-third of the hospital HCWs had experienced PSI; these were more likely to expect adverse events to recur (odds ratio, OR = 2.7–3.5). Regarding the hospital management of patient safety culture, the HCWs’ experiencing openly disclosed PSI was associated with one negative outcome, i.e. conflicts among colleagues (OR = 2.8), and one positive outcome, i.e. patients’ acceptance of their explanation and apologies (OR = 2.3). We found no associations for any other essential domains after disclosure. PSI experiences were strongly associated with psychological indicators of second victimhood, such as sadness, irritability, anxiety and depression (OR = 2.2–4.3), while providing support was not. The majority of the HCWs would like to participate in the suggested trainings (83.4%).ConclusionHCWs with PSI experiences reported poor hospital management of the patient safety culture, which might reflect they missed the opportunities to strengthen their resilience, especially during the COVID-19 pandemic.