Privacy Experts Research Articles

A Privacy-Preserving Medical Data Sharing Framework: Techniques, Applications, and Challenges

To improve patient outcomes and advance medical research, it is crucial that healthcare data are shared securely and effectively. It is difficult to communicate data while retaining confidentiality, though, due to the delicate nature of healthcare data and worries about patient privacy. Sensitive patient data must be safeguarded while facilitating secure data sharing, which calls for a framework that protects privacy. For implementing such a system, various methods like differential privacy, secure multi-party computation, and homomorphic encryption have been suggested. It is necessary to handle issues including interoperability problems, legal and ethical dilemmas, and technical difficulties. In order to create a framework for sharing medical data while protecting privacy, this study suggests a method that combines homomorphic encryption and blockchain technology. The suggested method offers a safe and effective means of exchanging and storing encrypted healthcare data while preserving data integrity and privacy. A multidisciplinary strategy combining cooperation between healthcare providers, data scientists, privacy experts, and regulatory agencies is necessary for the development and implementation of a medical data sharing framework that protects patient privacy. A privacy-preserving framework for medical data sharing can promote effective and secure data exchange for better healthcare outcomes by addressing the issues and utilizing the tools at hand.

COVID-19 considerations for SEC cybersecurity guidance, disclosure, enforcement, and parallel proceedings: navigating the new normal

Purpose To examine the COVID-19 pandemic’s effects on regulated entities within the context of cybersecurity, US Securities and Exchange Commission (SEC) compliance, and parallel proceedings. Design/methodology/approach Describes the SEC’s ability to conduct its operations within the telework environment, its commitment and ability to monitor the securities market, its enhanced monitoring of the adverse effects of SEC-regulated companies from COVID-19, its guidance to public companies of disclosure obligations related to cybersecurity risks and incidents, the SEC Office of Compliance and Examinations’s (OCIE’s) focus on broker-dealers’ and investment advisories’ cybersecurity preparedness, the role and activities of the SEC Division of Enforcement’s Cyber Unit, and parallel proceedings on cyberbreaches and incidents by different agencies, branches of government or private litigants. Findings SEC-regulated entities face many challenges in trying to maintain their ongoing business operations and infrastructure due to severe financial pressures, the threat of infection to employees and customers, and cybersecurity risks posed by remote operations from hackers and fraudsters. The SEC has reemphasized that its long-standing focus on cybersecurity and resiliency within the securities industry will continue, including ongoing vigilance over companies’ efforts to identify, assess, and address the inherent, heightened cybersecurity risks of teleworking and the resource reallocation that business need to sustain their operations until a safe and effective vaccine is developed for COVID-19. Originality/value Expert analysis and guidance from experienced lawyers with expertise in securities, litigation, government enforcement, information technology, data protection, privacy and cybersecurity.

Performance of Intensive Care Unit Severity Scoring Systems Across Different Ethnicities

Background: Despite wide utilisation of severity scoring systems for case-mix determination and benchmarking in the intensive care unit, the possibility of scoring bias across ethnicities has not been examined. Recent guidelines on the use of illness severity scores to inform triage decisions for allocation of scarce resources such as mechanical ventilation during the current COVID-19 pandemic warrant examination for possible bias in these models. We investigated the performance of three severity scoring systems (APACHE IVa, OASIS, SOFA) across ethnic groups in two large ICU databases in order to identify possible ethnicity-based bias.Method: Data from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care were analysed for score performance in Asians, African Americans, Hispanics and Whites after appropriate exclusions. Discrimination and calibration were determined for all three scoring systems in all four groups.Finding: While measurements of discrimination - area under the receiver operating characteristic curve (AUROC) - were significantly different among the groups, they did not display any discernible systematic patterns of bias. In contrast, measurements of calibration - standardised mortality ratio (SMR) - indicated persistent, and in some cases significant, patterns of difference between Hispanics and African Americans versus Asians and Whites. The differences between African Americans and Whites were consistently statistically significant. While calibrations were imperfect for all groups, the scores consistently demonstrated a pattern of over-predicting mortality for African Americans and Hispanics.Interpretation:The systematic differences in calibration across ethnic groups suggest that illness severity scores reflect bias in their predictions of mortality.Funding Statement: Funding: LAC is funded by the National Institute of Health through NIBIB R01 EB017205. There was no specific funding for this study.Declaration of Interests: RS received writing fees for healthcare reports from Crystallise UK Ltd. None of the other authors declare any conflict of interest.Ethics Approval Statement: Research using the eICU-CRD is exempt from institutional review board (IRB) approval due to the retrospective design, lack of direct patient intervention, and the security schema, for which the re-identification risk was certified as meeting safe harbour standards by an independent privacy expert (Privacert, Cambridge, MA) (Health Insurance Portability and Accountability Act Certification no. 1031219-2). The data in MIMIC-III has been previously de-identified, and the IRBs of the Massachusetts Institute of Technology (No. 0403000206) and BIDMC (2001-P-001699/14) both approved the use of the database for research.

Practical and Ethical Concerns in Implementing Enhanced Surveillance Methods to Improve Continuity of HIV Care: Qualitative Expert Stakeholder Study.

BackgroundRetention in HIV care is critical to maintaining viral suppression and preventing further transmission, yet less than 50% of people living with HIV in the United States are engaged in care. All US states have a funding mandate to implement Data-to-Care (D2C) programs, which use surveillance data (eg, laboratory, Medicaid billing) to identify out-of-care HIV-positive persons and relink them to treatment.ObjectiveThe purpose of this qualitative study was to identify and describe practical and ethical considerations that arise in planning for and implementing D2C.MethodsVia purposive sampling, we recruited 43 expert stakeholders—including ethicists, privacy experts, researchers, public health personnel, HIV medical providers, legal experts, and community advocates—to participate in audio-recorded semistructured interviews to share their perspectives on D2C. Interview transcripts were analyzed across a priori and inductively derived thematic categories.ResultsStakeholders reported practical and ethical concerns in seven key domains: permission and consent, government assistance versus overreach, privacy and confidentiality, stigma, HIV exceptionalism, criminalization, and data integrity and sharing.ConclusionsParticipants expressed a great deal of support for D2C, yet also stressed the role of public trust and transparency in addressing the practical and ethical concerns they identified.

Blind-sided by privacy? Digital contact tracing, the Apple/Google API and big tech\u2019s newfound role as global health policy makers

Since the outbreak of COVID-19, governments have turned their attention to digital contact tracing. In many countries, public debate has focused on the risks this technology poses to privacy, with advocates and experts sounding alarm bells about surveillance and mission creep reminiscent of the post 9/11 era. Yet, when Apple and Google launched their contact tracing API in April 2020, some of the world’s leading privacy experts applauded this initiative for its privacy-preserving technical specifications. In an interesting twist, the tech giants came to be portrayed as greater champions of privacy than some democratic governments. This article proposes to view the Apple/Google API in terms of a broader phenomenon whereby tech corporations are encroaching into ever new spheres of social life. From this perspective, the (legitimate) advantage these actors have accrued in the sphere of the production of digital goods provides them with (illegitimate) access to the spheres of health and medicine, and more worrisome, to the sphere of politics. These sphere transgressions raise numerous risks that are not captured by the focus on privacy harms. Namely, a crowding out of essential spherical expertise, new dependencies on corporate actors for the delivery of essential, public goods, the shaping of (global) public policy by non-representative, private actors and ultimately, the accumulation of decision-making power across multiple spheres. While privacy is certainly an important value, its centrality in the debate on digital contact tracing may blind us to these broader societal harms and unwittingly pave the way for ever more sphere transgressions.

Expert stakeholders’ perspectives on a Data-to-Care strategy for improving care among HIV-positive individuals incarcerated in jails

ABSTRACT Data-to-Care (D2C) uses surveillance data (e.g., laboratory, Medicaid billing) to identify out-of-care HIV-positive persons to re-link them to care. Most US states are implementing D2C, yet few studies have explored stakeholders’ perspectives on D2C, and none have addressed these perspectives in the context of D2C in jail. This article reports findings from qualitative, semi-structured interviews conducted with expert stakeholders regarding their perspectives on the ethical challenges of utilizing D2C to understand and improve continuity of care among individuals incarcerated in jails. Participants included 47 professionals with expertise in ethics and privacy, public health and HIV care, the criminal justice system, and community advocacy. While participants expressed a great deal of support for extending D2C to jails, they also identified many possible risks. Stakeholders discussed many issues specific to D2C in jails, such as heightened stigma in the jail setting, the need for training of jail staff and additional non-medical community-based resources, and the high priority of this vulnerable population. Many experts suggested that the actual likelihood of benefits and harms would depend on contextual details. Implementation of D2C in jails may require novel strategies to minimize risk of disclosing out-of-care patients’ HIV status.

Proposal of a Privacy Knowledge Base for Supporting Development of Privacy Friendly Software

Abstract With the spread of highly functional mobile devices, anyone can send messages in a simple manner. On the other hand, risks regarding the leakage of privacy information have increased in number. Software engineers must prevent the leakage of privacy information caused by problems of software systems. The need to tackle privacy issues in the early stage of the software development cycle is called Privacy by Design (PbD). However, a body of knowledge for privacy friendly software development has not been built. Since software developers are not always privacy experts, to provide such a body of knowledge is significant. This paper proposes a metamodel that manages knowledge for development of privacy friendly software and presents a knowledge base that embodies the metamodel by the already known knowledge. We also show an example using the knowledge base to present its usefulness.

Data Protection and Artificial Intelligence Law: Europe Australia Singapore - An Actual or Perceived Dichotomy?

Artificial Intelligence (AI) is moving so rapidly policy makers, regulators, governments and the legal profession are struggling to keep up. However, AI is not new and it has been used for more than two decades. Coupled with AI, personal data, along with cyber security law, and the challenges posed by the current legal frameworks are nothing short of immense. They are, in part, at odds with each other, and are doing very different things. This paper explores some of the challenges emerging in Australia, Europe and Singapore. The challenge of the interrelationship between personal data and AI arguably begins with who has manufactured the AI. Secondly who owns the AI. Another challenge that has also emerged is defining AI. Most people are able to understand what AI is and how it is beginning to impact the economy and our daily lives. However, there is no clear legal definition of AI, because AI is so nebulous. This burgeoning area of law is going to challenge society, privacy experts, regulators, innovators of technology, as there continues to be a collision between the two. Furthermore, the collection of personal data by AI challenges the notion of where responsibility lies. That is, AI may collect, use and disclose personal data at different points along the technology chain. It will be highlighted how the current data protection laws rather than promote AI projects, largely inhibit its development. This paper identifies some of the tensions between data protection law and AI. This paper argues that there is a need for an urgent and detailed understanding of the opportunities, legal and ethical issues associated with data protection and AI. Doing so will ensure an ongoing balance between the economic, social and human rights issues that are attached to the two areas of the law.

Developing and validating a common body of knowledge for information privacy

PurposeThis paper aims to present a common body of knowledge (CBK) for the field of information privacy, titled InfoPrivacy CBK. The purpose of the proposed CBK is to guide internet users to better understand the concept of information privacy and associate information privacy-related concepts. The InfoPrivacy CBK was created with an educational orientation to provide the basis for designing privacy awareness and training programs and organizing relevant educational material.Design/methodology/approachThe proposed CBK for information privacy was developed conceptually and includes five domains and four levels of analysis. It is illustrated with conceptual maps. The authors identified a variety of concepts related to information privacy and created a set of categories to categorize the concepts. They used, as inclusion criteria, both theoretical and practical information privacy aspects, so that the developed CBK can address the challenges of modern technologies for preserving information privacy.FindingsTo validate and refine the conceptually developed CBK, the authors conducted an empirical research, in which seven information privacy experts participated. The experts commented largely positively for the structure and content of InfoPrivacy CBK, as well as for the extent to which it achieves the intended educational goals.Research limitations/implicationsThe proposed InfoPrivacy CBK was validated by a limited number of information privacy experts, mainly due to the lengthy and in-depth participation that was required.Practical implicationsThe InfoPrivacy CBK can be used primarily by privacy awareness and training programs developers, such as organizations, data protection officers, the state, educational policy makers and teachers.Social implicationsInternet users will benefit from InfoPrivacy CBK by acquiring knowledge and skills from theoretically grounded training programs, which can enhance their awareness and critical thinking on issues related to the protection of their information privacy. This will lead to more privacy-aware online societies, communities, networks, etc.Originality/valueThis work intends to bridge the existing gap in the literature through the creation of a novel CBK for information privacy; information privacy is a field for which no such research effort has been recorded. This paper offers important knowledge in the field of information privacy, which could be useful to both technological education designers and learners (students, employees, etc.).

Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory.

BackgroundBiomedical research often requires large cohorts and necessitates the sharing of biomedical data with researchers around the world, which raises many privacy, ethical, and legal concerns. In the face of these concerns, privacy experts are trying to explore approaches to analyzing the distributed data while protecting its privacy. Many of these approaches are based on secure multiparty computations (SMCs). SMC is an attractive approach allowing multiple parties to collectively carry out calculations on their datasets without having to reveal their own raw data; however, it incurs heavy computation time and requires extensive communication between the involved parties.ObjectiveThis study aimed to develop usable and efficient SMC applications that meet the needs of the potential end-users and to raise general awareness about SMC as a tool that supports data sharing.MethodsWe have introduced distributed statistical computing (DSC) into the design of secure multiparty protocols, which allows us to conduct computations on each of the parties’ sites independently and then combine these computations to form 1 estimator for the collective dataset, thus limiting communication to the final step and reducing complexity. The effectiveness of our privacy-preserving model is demonstrated through a linear regression application.ResultsOur secure linear regression algorithm was tested for accuracy and performance using real and synthetic datasets. The results showed no loss of accuracy (over nonsecure regression) and very good performance (20 min for 100 million records).ConclusionsWe used DSC to securely calculate a linear regression model over multiple datasets. Our experiments showed very good performance (in terms of the number of records it can handle). We plan to extend our method to other estimators such as logistic regression.

Data Protection and Artificial Intelligence Law: Europe Australia Singapore - An Actual or Perceived Dichotomy

Artificial Intelligence (AI) is moving so rapidly policy makers, regulators, governments and the legal profession are struggling to keep up. However, AI is not new and it has been used for more than two decades. Coupled with AI, personal data, along with cyber security law, and the challenges posed by the current legal frameworks are nothing short of immense. They are, in part, at odds with each other, and are doing very different things. This paper explores some of the challenges emerging in Australia, Europe and Singapore. The challenge of the interrelationship between personal data and AI arguably begins with who has manufactured the AI. Secondly, who owns the AI. Another challenge that has also emerged is defining AI. Most people are able to understand what AI is and how it is beginning to impact the economy and our daily lives. However, there is no clear legal definition of AI, because AI is so nebulous. This burgeoning area of law is going to challenge society, privacy and economic experts, regulators, innovators of technology, as there continues to be a collision between them. Furthermore, the collection of personal data by AI challenges the notion of where responsibility lies. That is, AI may collect, use and disclose personal data at different points along the technology chain. It will be highlighted how the current data protection laws rather than promote AI projects, largely inhibit its development. This paper identifies some of the tensions between data protection law and AI. This paper argues that there is a need for an urgent and detailed understanding of the opportunities, legal and ethical issues associated with data protection and AI. Doing so will ensure an ongoing balance between the economic and social issues that are attached to the two areas of the law.

Weapons of mass distraction

The Circle invites an ever closer look at the ethos of current and emerging surveillance technology. Dave Eggers’ novel foreshadowed the culminating moments in 2018, when high-powered social media platforms generated a maelstrom of controversy in the US and UK and then nothing changed. Concern over the integrity of electoral processes around the globe has risen to new heights, as privacy experts warn that unfettered growth of surveillance capitalism could change democracy forever. Far from a case of unintended consequences run amok, corporate tech executives admit that continual mining of personal data for unrestricted use by corporations and political operatives that specialise in psychological manipulation were part of the original design. The dark side of all this connectivity as highlighted by the ruckus over Cambridge Analytica places mainstream news producers squarely under the microscope. This article examines the wilderness between the goal of reporting in the public’s interest and the current role of news organisations.

Turtles, Locks, and Bathrooms: Understanding Mental Models of Privacy Through Illustration

Abstract Are the many formal definitions and frameworks of privacy consistent with a layperson’s understanding of privacy? We explored this question and identified mental models and metaphors of privacy, conceptual tools that can be used to improve privacy tools, communication, and design for everyday users. Our investigation focused on a qualitative analysis of 366 drawings of privacy from laypeople, privacy experts, children, and adults. Illustrators all responded to the prompt “What does privacy mean to you?” We coded each image for content, identifying themes from established privacy frameworks and defining the visual and conceptual metaphors illustrators used to model privacy. We found that many non-expert drawings illustrated a strong divide between public and private physical spaces, while experts were more likely to draw nuanced data privacy spaces. Young children’s drawings focused on bedrooms, bathrooms, or cheating on schoolwork, and seldom addressed data privacy. The metaphors, themes, and symbols identified by these findings can be used for improving privacy communication, education, and design by inspiring and informing visual and conceptual strategies for reaching laypeople.

Users' Information Privacy Concerns and Privacy Protection Behaviors in Social Networks

ABSTRACTThe immense popularity of social networks has overshadowed the potential concerns, especially related to information privacy. Despite huge debates among privacy experts in recent times, seminal works have explored information privacy concerns from the perspective of social network users. In response, this research aims to understand the impact of antecedents of users' information privacy concerns (UIPC) on privacy protection behavior (PPB) in social networks. Drawing from the Social Cognitive Theory and Protection Motivation Theory, the research model was analyzed using structural equation modeling. The empirical results show that perceived vulnerability, perceived severity, and self-efficacy significantly influences UIPC. However, rewards and response efficacy did not significantly contribute towards UIPC. The linkage between UIPC and PPB was found to be statistically significant. This research article will offer newer insights for conceptual development and assist the social network's providers in redesigning their privacy protection strategies for safeguarding users from privacy threats.

Internet of Things ('IOT') - Privacy Concerns

With all the convenience that comes with IoT, it is not suprising that customers are willing to share their logistics, data and very personal and detailed information with companies without much credence to privacy. However, privacy experts in the IoT community are concerned about data misuse, misappropriation and collection of irrelevant data. Although, the tradeoff from the customer’s perspective is convenient, there are those who believe that with such few built-in security features in today’s everyday IoT, which arguably, is in its nascent stage, a privacy and security catastrophe is imminent.

The valuation of privacy premium features for smartphone apps: The influence of defaults and expert recommendations

This study examines the impact of privacy defaults and expert recommendations on smartphone users' willingness to pay for “privacy-enhanced” features on paid applications using a 2 (privacy premium default/no privacy premium default) x 2 (privacy expert recommendation/non-privacy expert recommendation) experimental design. Participants (N = 309) configured four paid apps with respect to privacy features. Selecting premium privacy features was associated with an increased cost, while removing premium privacy features reduced the cost of the application. Replicating findings from behavioral economics on default modes in decision-making, we found that participants presented with apps with privacy premium default features were more likely to retain the more expensive privacy features. However, the recommendation source did not have a significant effect on this relationship. We discuss how these findings extend existing work on users' decision-making process around privacy and suggest potential avenues for nudging users’ privacy behaviors on mobile devices.

Leveraging Crowd for Collecting and Maintaining Educational Resources for Privacy Learning

As the Internet grows in importance, it is vital to develop methods and techniques for educating end-users to improve their awareness of online privacy. Web-based education tools have been proven effective in many domains and have been increasingly adopted by many online professional and educational services. However, the design and development of Web-based education tools for online privacy is still in its early stage. The traditional solutions always involve privacy experts who have sophisticated expertise. However, it is not clear how inspiring and effective these education tools are to general users of varying backgrounds. Furthermore, such involvement can make the tool development costly. In this paper, we discuss our experiences of designing a quiz-based privacy learning tool by leveraging the wisdom of the crowd on Amazon Mechanic Turk for two purposes: (1) designing the quiz questions for the tool; and (2) evaluating and assessing the effectiveness of the tool. Empirical study demonstrates that the crowd can provide high-quality educational materials for privacy learning.

Personalized Consent Flow in Contemporary Data Sharing for Medical Research: A Viewpoint.

Background Health data personally collected by individuals with wearable devices and smartphones is becoming an important data source for healthcare, but also for medical research. Objective To describe a new consent model that allows people to control their personally collected health data and determine to what extent they want to share these for research purposes. Methods We developed, in close collaboration with patients, researchers, healthcare professionals, privacy experts, and an accredited Medical Ethical Review Committee, an innovative concept called “personalized consent flow” within a research platform connected to a personal health record. The development was an iterative process with informal meetings, semistructured interviews, and surveys. The final concept of the personalized consent flow was reviewed by patients and improved and approved by the same patients in a focus group. Results This concept could result in optimal control for individual users, since they will answer questions about how they will share data. Furthermore, it enables users to collect data for specific studies and add expiration dates to their data. This work facilitates further discussion about dynamic and personalized consent. A pilot study with the personalized consent model is currently being carried out.

Using Visualization to Explore Original and Anonymized LBSN Data

AbstractWe present GSUVis, a visualization tool designed to provide better understanding of location‐based social network (LBSN) data. LBSN data is one of the most important sources of information for transportation, marketing, health, and public safety. LBSN data consumers are interested in accessing and analysing data that is as complete and as accurate as possible. However, LBSN data contains sensitive information about individuals. Consequently, data anonymization is of critical importance if this data is to be made available to consumers. However, anonymization commonly reduces the utility of information available. Working with privacy experts, we designed GSUVis a visual analytic tool to help experts better understand the effects of anonymization techniques on LBSN data utility. One of GSUVis's primary goals is to make it possible for people to use LBSN data, without requiring them to gain deep knowledge about data anonymization. To inform the design of GSUVis, we interviewed privacy experts, and collected their tasks and system requirements. Based on this understanding, we designed and implemented GSUVis. It applies two anonymization algorithms for social and location trajectory data to a real‐world LBSN dataset and visualizes the data both before and after anonymization. Through feedback from domain experts, we reflect on the effectiveness of GSUVis and the impact of anonymization using visualization.

Greater goods?: Direct-to-consumer testing companies are making a broader case for societal benefits, but not everyone is sold.

Greater goods?: Direct-to-consumer testing companies are making a broader case for societal benefits, but not everyone is sold.