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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 -
Annual Review of Biomedical Data Science Aug 2022Genomics data are important for advancing biomedical research, improving clinical care, and informing other disciplines such as forensics and genealogy. However, privacy... (Review)
Review
Genomics data are important for advancing biomedical research, improving clinical care, and informing other disciplines such as forensics and genealogy. However, privacy concerns arise when genomic data are shared. In particular, the identifying nature of genetic information, its direct relationship to health status, and the potential financial harm and stigmatization posed to individuals and their blood relatives call for a survey of the privacy issues related to sharing genetic and related data and potential solutions to overcome these issues. In this work, we provide an overview of the importance of genomic privacy, the information gleaned from genomics data, the sources of potential private information leakages in genomics, and ways to preserve privacy while utilizing the genetic information in research. We discuss the relationship between trust in the scientific community and protecting privacy, illuminating a future roadmap for data sharing and study participation.
Topics: Genome; Genomics; Humans; Information Dissemination; Privacy; Trust
PubMed: 35508070
DOI: 10.1146/annurev-biodatasci-122120-021311 -
The Journal of Adolescent Health :... Oct 2023
Topics: Humans; Parental Consent; Privacy; Relational Autonomy
PubMed: 37716714
DOI: 10.1016/j.jadohealth.2023.07.004 -
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 -
Bundesgesundheitsblatt,... Sep 2022
Topics: Environment; Genetic Privacy; Genetic Testing; Germany
PubMed: 35776150
DOI: 10.1007/s00103-022-03559-2 -
Bundesgesundheitsblatt,... Sep 2022
Topics: Environment; Genetic Privacy; Genetic Testing; Germany
PubMed: 36048213
DOI: 10.1007/s00103-022-03565-4 -
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