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Journal of Nuclear Medicine Technology Dec 2019The Health Insurance Portability and Accountability Act (HIPAA) of 1996 has made an impact on the operation of health-care organizations. HIPAA includes 5 titles, and... (Review)
Review
The Health Insurance Portability and Accountability Act (HIPAA) of 1996 has made an impact on the operation of health-care organizations. HIPAA includes 5 titles, and its regulations are complex. Many are familiar with the HIPAA aspects that address protection of the privacy and security of patients' medical records. There are new rules to HIPAA that address the implementation of electronic medical records. HIPAA provides rules for protected health information (PHI) and what should be protected and secured. The privacy rule regulates the use and disclosure of PHI and sets standards that an entity working with health data must follow to protect patients' private medical information. The HIPAA security rule complements the privacy rule and requires entities to implement physical, technical, and administrative safeguards to protect the privacy of PHI. This article-part 1 of a 2-part series-is a refresher on HIPAA, its history, its rules, its implications, and the role that imaging professionals play.
Topics: Guideline Adherence; Health Information Exchange; Health Insurance Portability and Accountability Act; Privacy; United States
PubMed: 31182664
DOI: 10.2967/jnmt.119.227819 -
American Journal of Orthodontics and... Oct 2020
Topics: Confidentiality; Privacy
PubMed: 32988570
DOI: 10.1016/j.ajodo.2020.07.004 -
Philosophical Transactions. Series A,... Sep 2018This position paper observes how different technical and normative conceptions of privacy have evolved in parallel and describes the practical challenges that these... (Review)
Review
This position paper observes how different technical and normative conceptions of privacy have evolved in parallel and describes the practical challenges that these divergent approaches pose. Notably, past technologies relied on intuitive, heuristic understandings of privacy that have since been shown not to satisfy expectations for privacy protection. With computations ubiquitously integrated in almost every aspect of our lives, it is increasingly important to ensure that privacy technologies provide protection that is in line with relevant social norms and normative expectations. Similarly, it is also important to examine social norms and normative expectations with respect to the evolving scientific study of privacy. To this end, we argue for a rigorous analysis of the mapping from normative to technical concepts of privacy and vice versa. We review the landscape of normative and technical definitions of privacy and discuss specific examples of gaps between definitions that are relevant in the context of privacy in statistical computation. We then identify opportunities for overcoming their differences in the design of new approaches to protecting privacy in accordance with both technical and normative standards.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.
Topics: Attitude; Privacy
PubMed: 30082304
DOI: 10.1098/rsta.2017.0358 -
Nature Reviews. Genetics Jul 2022Recent developments in a variety of sectors, including health care, research and the direct-to-consumer industry, have led to a dramatic increase in the amount of... (Review)
Review
Recent developments in a variety of sectors, including health care, research and the direct-to-consumer industry, have led to a dramatic increase in the amount of genomic data that are collected, used and shared. This state of affairs raises new and challenging concerns for personal privacy, both legally and technically. This Review appraises existing and emerging threats to genomic data privacy and discusses how well current legal frameworks and technical safeguards mitigate these concerns. It concludes with a discussion of remaining and emerging challenges and illustrates possible solutions that can balance protecting privacy and realizing the benefits that result from the sharing of genetic information.
Topics: Genome; Genomics; Privacy
PubMed: 35246669
DOI: 10.1038/s41576-022-00455-y -
IEEE Transactions on Pattern Analysis... Feb 2022Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together. In contrast, in single-task learning (STL) each individual task is learned...
Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together. In contrast, in single-task learning (STL) each individual task is learned independently. MTL often leads to better trained models because they can leverage the commonalities among related tasks. However, because MTL algorithms can "leak" information from different models across different tasks, MTL poses a potential security risk. Specifically, an adversary may participate in the MTL process through one task and thereby acquire the model information for another task. The previously proposed privacy-preserving MTL methods protect data instances rather than models, and some of them may underperform in comparison with STL methods. In this paper, we propose a privacy-preserving MTL framework to prevent information from each model leaking to other models based on a perturbation of the covariance matrix of the model matrix. We study two popular MTL approaches for instantiation, namely, learning the low-rank and group-sparse patterns of the model matrix. Our algorithms can be guaranteed not to underperform compared with STL methods. We build our methods based upon tools for differential privacy, and privacy guarantees, utility bounds are provided, and heterogeneous privacy budgets are considered. The experiments demonstrate that our algorithms outperform the baseline methods constructed by existing privacy-preserving MTL methods on the proposed model-protection problem.
Topics: Algorithms; Learning; Privacy
PubMed: 32780696
DOI: 10.1109/TPAMI.2020.3015859 -
Journal of Medical Ethics May 2022Significant advancements towards a future of big data genomic medicine, associated with large-scale public dataset repositories, intensify dilemmas of genomic privacy....
Significant advancements towards a future of big data genomic medicine, associated with large-scale public dataset repositories, intensify dilemmas of genomic privacy. To resolve dilemmas adequately, we need to understand the relative force of the competing considerations that make them up. Attitudes towards genomic privacy are complex and not well understood; understanding is further complicated by the vague claim of 'genetic exceptionalism'. In this paper, we distinguish between consequentialist and non-consequentialist privacy interests: while the former are concerned with harms secondary to exposure, the latter represent the interest in a private sphere for its own sake, as an essential component of human dignity. Empirical studies of attitudes towards genomic privacy have almost never targeted specifically this important dignitary component of the privacy interest. In this paper we first articulate the question of a non-consequentialist genomic privacy interest, and then present results of an empirical study that probed people's attitudes towards that interest. This was done via comparison to other non-consequentialist privacy interests, which are more tangible and can be more easily assessed. Our results indicate that the non-consequentialist genomic privacy interest is rather weak. This insight can assist in adjudicating dilemmas involving genomic privacy.
Topics: Genomics; Humans; Privacy; Respect
PubMed: 33910975
DOI: 10.1136/medethics-2020-106979 -
Current Opinion in Psychology Feb 2020Psychological targeting describes the practice of extracting people's psychological profiles from their digital footprints (e.g. their Facebook Likes, Tweets or credit... (Review)
Review
Psychological targeting describes the practice of extracting people's psychological profiles from their digital footprints (e.g. their Facebook Likes, Tweets or credit card records) in order to influence their attitudes, emotions or behaviors through psychologically informed interventions at scale. We discuss how the increasingly blurred lines between public and private information, and the continuation of the outdated practices of notice and consent, challenge traditional conceptualizations of privacy in the context of psychological targeting. Drawing on the theory of contextual integrity, we argue that it is time to rethink privacy and move beyond the questions of who collects what data to how the data are being used. Finally, we suggest that regulations of psychological targeting should be accompanied by a mindset that fosters (1) privacy by design to make it easy for individuals to act in line with their privacy goals, as well as (2) disclosure by choice, to allow individuals to freely decide whether and when they might be willing to forsake their privacy for better service.
Topics: Humans; Marketing; Persuasive Communication; Privacy; Social Media
PubMed: 31563799
DOI: 10.1016/j.copsyc.2019.08.010 -
Genetic Testing and Molecular Biomarkers Sep 2023
Topics: Humans; Privacy; Genetic Testing; Genetic Privacy; Confidentiality
PubMed: 37702624
DOI: 10.1089/gtmb.2023.29076.persp -
Journal of the American Psychoanalytic... Oct 2023
Topics: Humans; Privacy; Writing; Sexual Behavior
PubMed: 38140968
DOI: 10.1177/00030651231208340 -
Studies in Health Technology and... May 2022Synthetic data has been more and more used in the last few years. While its applications are various, measuring its utility and privacy is seldom an easy task. Since...
Synthetic data has been more and more used in the last few years. While its applications are various, measuring its utility and privacy is seldom an easy task. Since there are different methods of evaluating these issues, which are dependent on data types, use cases and purpose, a generic method for evaluating utility and privacy does not exist at the moment. So, we introduced a compilation of the most recent methods for evaluating privacy and utility into a single executable in order to create a report of the similarities and potential privacy breaches between two datasets, whether it is related to synthetic or not. We catalogued 24 different methods, from qualitative to quantitative, column-wise or table-wise evaluations. We hope this resource can help scientists and industries get a better grasp of the synthetic data they have and produce more easily and a better basis to create a new, more broad method for evaluating dataset similarities.
Topics: Organizations; Privacy
PubMed: 35612009
DOI: 10.3233/SHTI220389