Does Big Data Change the Privacy Landscape? A Review of the Issues

Abstract

The current data revolution is changing the conduct of social science research as increasing amounts of digital and administrative data become accessible for use. This new data landscape has created significant tension around data privacy and confidentiality. The risk-utility theory and models underpinning statistical disclosure limitation may be too restrictive for providing data confidentially owing to the growing volumes and varieties of data and the evolving privacy policies. Science and society need to move to a trust-based approach from which both researchers and participants benefit. This review discusses the explosive growth of the new data sources and the parallel evolution of privacy policy and governance, with a focus on access to data for research. We provide a history of privacy policy, statistical disclosure limitation research, and record linkage in the context of this brave new world of data.

Big Data Needs and Challenges to Advance Research on Racial and Ethnic Inequities in Maternal and Child Health

“…Challenges to HIPAA privacy rules include the potential ability to re-identify participants via data triangulation from other data sources and the limited protections for data collected by nonhealth entities, such as user-generated information about health collected by phone apps or Internet searches that are not about health but may allow inferences about health, such as purchasing behaviors that reveal one's pregnancy status (Price & Cohen, 2019). Although there are laws that can limit access to private data, researchers may need to build in additional protections beyond HIPAA to ensure data are protected (Keller, Shipp, & Schroeder, 2016;Snoke & Bowen, 2020). Researchers using big data should be transparent about the risks associated with data privacy and security and clearly communicate to participants how they plan to mitigate those risks.…”

Plausible deniability for privacy-preserving data synthesis

“…The generative model we presented in Section 3.1 uses a similar technique. This and related work have given rise to a substantial body of research on the release of synthetic data [24,12]. Such techniques have achieved significant deployment; for example, they have been adopted by the U.S. Census Bureau [20,27].…”

Hope, hype and harms of Big Data

“…Tenth, in assessing effects of interventions, large sample sizes will often generate highly significant P values for effect sizes which remain very marginal and contestable . Last, privacy concerns may limit access to much needed but potentially patient‐identifiable data, given recent scandals involving Facebook and Cambridge Analytica.…”