Balancing Benefits and Risks of Immortal Data

Zarate, Oscar A.; Brody, Julia Green; Brown, Phil; Ramírez‐Andreotta, Mónica D.; Perovich, Laura J.; Matz, Jacob

doi:10.1002/hast.523

Cited by 40 publications

(26 citation statements)

References 16 publications

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“…Four papers discussed data linkage, that is, the possibility of automatically obtaining, linking, and disclosing personal and sensitive information as an important cause of discrimination. Two articles [19,91] described how the use of electronic health records could result in the automatic disclosure of sensitive data without the patient's explicit agreement or to re-identification. Others [64,74] also highlighted that discrimination is not created by a data collection system (such as social and health registries) in itself, but is made easier by the linkage and aggregation potentiality embedded in the data.…”

Section: Data Linkage and Aggregationmentioning

confidence: 99%

Big Data and discrimination: perils, promises and solutions. A systematic review

2019

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Big Data has been described as a "one-size-fits-all (so long as it's triple XL) answer" [24] to solve some of the most challenging problems in the fields of climate change, healthcare, education and criminology. This may explain why it has become the buzzword of the decade. Big Data is a very complex and extensive phenomenon that has had fluctuating meanings since its appearance in the early 2010's [86]. Traditionally it has been defined in terms of four dimensions (the four V's of Big Data): volume, velocity,

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Section: Data Linkage and Aggregationmentioning

confidence: 99%

Big Data and discrimination: perils, promises and solutions. A systematic review

2019

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“…Researchers who violate these pledges, or who do not offer privacy protections in future studies, will risk suppressing research participation among people who fear loss of privacy. Some people are comfortable with open data sharing (Zarate et al 2016), and others may be willing to accept low to moderate privacy risks for the benefit of public health. However, requiring consent for permissive data sharing could negatively affect participation of racial and ethnic minorities, populations that are already underrepresented in health research (Konkel 2015), and overburdened by diseases with environmental triggers, such as asthma (Forno and Celedón 2012).…”

Section: Technical and Policy Solutionsmentioning

confidence: 99%

“…In addition, EH studies can include genetic, medical, or household data that are themselves vulnerable to re-ID, creating disclosure risks for the entire data set. Loss of privacy from re-ID could result in stigma for individuals and communities; affect property values, insurance, employability, and legal obligations; or reveal embarrassing or illegal activity (Goho 2016;Zarate et al 2016). It could damage trust in research, harming the study and research more generally.…”

Section: Introductionmentioning

confidence: 99%

Privacy Risks of Sharing Data from Environmental Health Studies

Boronow

Perovich

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et al. 2020

Environ Health Perspect

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BACKGROUND: Sharing research data uses resources effectively; enables large, diverse data sets; and supports rigor and reproducibility. However, sharing such data increases privacy risks for participants who may be re-identified by linking study data to outside data sets. These risks have been investigated for genetic and medical records but rarely for environmental data. OBJECTIVES: We evaluated how data in environmental health (EH) studies may be vulnerable to linkage and we investigated, in a case study, whether environmental measurements could contribute to inferring latent categories (e.g., geographic location), which increases privacy risks. METHODS: We identified 12 prominent EH studies, reviewed the data types collected, and evaluated the availability of outside data sets that overlap with study data. With data from the Household Exposure Study in California and Massachusetts and the Green Housing Study in Boston, Massachusetts, and Cincinnati, Ohio, we used k-means clustering and principal component analysis to investigate whether participants' region of residence could be inferred from measurements of chemicals in household air and dust. RESULTS: All 12 studies included at least two of five data types that overlap with outside data sets: geographic location (9 studies), medical data (9 studies), occupation (10 studies), housing characteristics (10 studies), and genetic data (7 studies). In our cluster analysis, participants' region of residence could be inferred with 80%-98% accuracy using environmental measurements with original laboratory reporting limits. DISCUSSION: EH studies frequently include data that are vulnerable to linkage with voter lists, tax and real estate data, professional licensing lists, and ancestry websites, and exposure measurements may be used to identify subgroup membership, increasing likelihood of linkage. Thus, unsupervised sharing of EH research data potentially raises substantial privacy risks. Empirical research can help characterize risks and evaluate technical solutions. Our findings reinforce the need for legal and policy protections to shield participants from potential harms of re-identification from data sharing.

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“…In this era of precision medicine, eHealth—the use of information and communication technology (ICT) for health—will become indispensable. However, there are many problems related to eHealth, including security, informed consent for data-sharing and secondary use, standardization, structuring, and deidentification (The Global Alliance for Genomics and Health, 2016 ; Zarate et al, 2016 ). Such new ethical issues are inevitable to establish a relationship of mutual trust between research participants and researchers.…”

Section: Introduction—new Initiatives In the Medical Treatment And Rementioning

confidence: 99%

Authentication of Patients and Participants in Health Information Exchange and Consent for Medical Research: A Key Step for Privacy Protection, Respect for Autonomy, and Trustworthiness

2018

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Genome and other data are already being used in areas including cancer and rare diseases. Data-sharing and secondary uses are likely to become much broader and far more extensive; thus, obtaining proper consent for these new uses of data is an important issue. Obtaining consent through online methods may be an option to overcome the problems associated with one-off, paper-based informed consent. When the process of obtaining consent takes place remotely, authentication must be assured. Patients may also choose to store some of their own information online, such as genetic information, and allow healthcare professionals to access this data. In this health information transfer and exchange process, it is vital that anyone accessing this information be correctly authenticated to protect patients' privacy. In this article, we first clarified that authentication has two roles: i.e., not only to prevent impersonation but also to prove intent, which is a vital step to ensure that medical research and health information exchange are conducted ethically. We then set out methods of authentication. As a result, we were able to make suggestions about the requirements for authentication and a possible method of authentication for these purposes. We considered problems of biometrics and recommended two-factor authentication without biometrics as a workable solution. However, three-factor authentication including biometrics seems likely to be used once biometrics become more common.

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Balancing Benefits and Risks of Immortal Data

Cited by 40 publications

References 16 publications

Big Data and discrimination: perils, promises and solutions. A systematic review

Big Data and discrimination: perils, promises and solutions. A systematic review

Privacy Risks of Sharing Data from Environmental Health Studies

Authentication of Patients and Participants in Health Information Exchange and Consent for Medical Research: A Key Step for Privacy Protection, Respect for Autonomy, and Trustworthiness

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