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The goal of "personalized medicine" relies upon defining the genetic variation responsible for disease susceptibility and response to therapy [1]. For most common human diseases, the contribution of a single [...]

Direct-to-consumer genetic testing is marketed as a tool to uncover ancestry and kin. Recent studies of actual and potential users have demonstrated that individuals' responses to the use of these tests for these purposes are complex, with privacy, disruptive consequences, potential for misuse, and secondary use by law enforcement cited as potential concerns. We conducted six focus groups with a diverse sample of participants (n = 62) who were aware of but had not used direct-to-consumer genetic tests, in an effort to understand more about what people considering these tests think about the potential value, risks, and benefits of such testing, taking into account use by third parties, such as potential kin and law enforcement. Participants differed widely in the perceived value of direct-to-consumer genetic tests for ancestry and kinship information for their own lives, including the desirability of contact with previously unknown relatives. Some perceived ancestry testing as mere curiosity or entertainment, while others, particularly those who had gaps in their family history, few living relatives, or who were adopted, saw greater value. Concerns about intrusion into one's life by purported kin and control of data were widespread, with many participants expressing concern about secondary uses of data that could harm users or their families. The use of direct-to-consumer genetic tests data for forensic genealogy elicited a particularly wide array of reactions, both spontaneously and in response to specific discussion prompts, mirroring the current public debate about law enforcement access to such data. The themes uncovered through our investigation warrant specific attention in the continued development of the science, policy, and practice of commercial direct-to-consumer genetic testing. [ABSTRACT FROM AUTHOR]

Background: Millions of people have undergone direct-to-consumer genetic testing (DTC-GT), but little is known about individuals' motivations and experiences (e.g., discussion topics and emotions after obtaining the test results) in engaging with DTC-GT services. Previous studies either involved only a small number of DTC-GT consumers or were based on hypothetical scenarios. Objective: Our study aimed to fill this gap by investigating online discussions about DTC-GT that developed naturally among tens of thousands of social media users. Methods: We focused on the posts that were published in the r/23andme and r/AncestryDNA subreddits, which correspond to the two companies with the largest number of consumers in the DTC-GT market. We applied computational methods to infer and examine the topics discussed, temporal trends in posting rates and themes (e.g., aggregation of related topics), and emotions expressed in these online forums. Results: We collected 157,000 posts published by 16,500 Reddit users between 2013 and 2019. We found that the posting rates increased sharply after popular promotional events (e.g., each Amazon Prime Day and Black Friday) and most posts were inquiries into, or status updates about, testing progress. The inferred themes of Ancestral Origin and Kinship/Feelings were the two most frequently discussed, while discussions about the Health Risks theme focused primarily on submitting DTC-GT raw data to third parties for interpretation. The Kinship/Feelings theme exhibited the largest range of emotional response. A qualitative review of the posts with extreme emotions showed that some people became excited because they found their biological parents or other kin, while others became upset because they unexpectedly found that their parents or other kin were not biologically related to them. Conclusion: This research demonstrates that online social media platforms can serve as a rich resource for characterizing actual DTC-GT experiences. The findings suggest that DTC-GT consumers' purchasing behaviors are associated with societal events and that future investigations should consider how DTC-GT challenges our understanding of kinship structure and function, genomic privacy, and the interpretation of health risks. [ABSTRACT FROM AUTHOR]

Concerns about genetic privacy affect individuals’ willingness to accept genetic testing in clinical care and to participate in genomics research. To learn what is already known about these views, we conducted a systematic review, which ultimately analyzed 53 studies involving the perspectives of 47,974 participants on real or hypothetical privacy issues related to human genetic data. Bibliographic databases included MEDLINE, Web of Knowledge, and Sociological Abstracts. Three investigators independently screened studies against predetermined criteria and assessed risk of bias. The picture of genetic privacy that emerges from this systematic literature review is complex and riddled with gaps. When asked specifically “are you worried about genetic privacy,” the general public, patients, and professionals frequently said yes. In many cases, however, that question was posed poorly or only in the most general terms. While many participants expressed concern that genomic and medical information would be revealed to others, respondents frequently seemed to conflate privacy, confidentiality, control, and security. People varied widely in how much control they wanted over the use of data. They were more concerned about use by employers, insurers, and the government than they were about researchers and commercial entities. In addition, people are often willing to give up some privacy to obtain other goods. Importantly, little attention was paid to understanding the factors–sociocultural, relational, and media—that influence people’s opinions and decisions. Future investigations should explore in greater depth which concerns about genetic privacy are most salient to people and the social forces and contexts that influence those perceptions. It is also critical to identify the social practices that will make the collection and use of these data more trustworthy for participants as well as to identify the circumstances that lead people to set aside worries and decide to participate in research. [ABSTRACT FROM AUTHOR]

Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing. [ABSTRACT FROM AUTHOR]

Health information technologies facilitate the collection of massive quantities of patient-level data. A growing body of research demonstrates that such information can support novel, large-scale biomedical investigations at a fraction of the cost of traditional prospective studies. While healthcare organizations are being encouraged to share these data in a de-identified form, there is hesitation over concerns that it will allow corresponding patients to be re-identified. Currently proposed technologies to anonymize clinical data may make unrealistic assumptions with respect to the capabilities of a recipient to ascertain a patients identity. We show that more pragmatic assumptions enable the design of anonymization algorithms that permit the dissemination of detailed clinical profiles with provable guarantees of protection. We demonstrate this strategy with a dataset of over one million medical records and show that 192 genotype-phenotype associations can be discovered with fidelity equivalent to non-anonymized clinical data. [ABSTRACT FROM AUTHOR]

Background: Privacy legislation in most jurisdictions allows the disclosure of health data for secondary purposes without patient consent if it is de-identified. Some recent articles in the medical, legal, and computer science literature have argued that de-identification methods do not provide sufficient protection because they are easy to reverse. Should this be the case, it would have significant and important implications on how health information is disclosed, including: (a) potentially limiting its availability for secondary purposes such as research, and (b) resulting in more identifiable health information being disclosed. Our objectives in this systematic review were to: (a) characterize known re-identification attacks on health data and contrast that to re-identification attacks on other kinds of data, (b) compute the overall proportion of records that have been correctly re-identified in these attacks, and (c) assess whether these demonstrate weaknesses in current de-identification methods. Methods and Findings: Searches were conducted in IEEE Xplore, ACM Digital Library, and PubMed. After screening, fourteen eligible articles representing distinct attacks were identified. On average, approximately a quarter of the records were re-identified across all studies (0.26 with 95% CI 0.046-0.478) and 0.34 for attacks on health data (95% CI 0-0.744). There was considerable uncertainty around the proportions as evidenced by the wide confidence intervals, and the mean proportion of records re-identified was sensitive to unpublished studies. Two of fourteen attacks were performed with data that was de-identified using existing standards. Only one of these attacks was on health data, which resulted in a success rate of 0.00013. Conclusions: The current evidence shows a high re-identification rate but is dominated by small-scale studies on data that was not de-identified according to existing standards. This evidence is insufficient to draw conclusions about the efficacy of de-identification methods. [ABSTRACT FROM AUTHOR]

Background: Privacy legislation in most jurisdictions allows the disclosure of health data for secondary purposes without patient consent if it is de-identified. Some recent articles in the medical, legal, and computer science literature have argued that de-identification methods do not provide sufficient protection because they are easy to reverse. Should this be the case, it would have significant and important implications on how health information is disclosed, including: (a) potentially limiting its availability for secondary purposes such as research, and (b) resulting in more identifiable health information being disclosed. Our objectives in this systematic review were to: (a) characterize known re-identification attacks on health data and contrast that to re-identification attacks on other kinds of data, (b) compute the overall proportion of records that have been correctly re-identified in these attacks, and (c) assess whether these demonstrate weaknesses in current de-identification methods., Methods and Findings: Searches were conducted in IEEE Xplore, ACM Digital Library, and PubMed. After screening, fourteen eligible articles representing distinct attacks were identified. On average, approximately a quarter of the records were re-identified across all studies (0.26 with 95% CI 0.046-0.478) and 0.34 for attacks on health data (95% CI 0-0.744). There was considerable uncertainty around the proportions as evidenced by the wide confidence intervals, and the mean proportion of records re-identified was sensitive to unpublished studies. Two of fourteen attacks were performed with data that was de-identified using existing standards. Only one of these attacks was on health data, which resulted in a success rate of 0.00013., Conclusions: The current evidence shows a high re-identification rate but is dominated by small-scale studies on data that was not de-identified according to existing standards. This evidence is insufficient to draw conclusions about the efficacy of de-identification methods.