Biostatistics Weekly Seminar

Privacy is a social construct that is realized in different ways under varying situations in healthcare and biomedical research. In this respect, context is king, such that the manner by which privacy can be injected into a system is dependent on a variety of factors that influence the environment. This is particularly the case when considering privacy in the big data age or what one might call, big privacy. As computing becomes increasingly cheap and ever more ubiquitous, it seems as though upholding privacy is an impossible task. This notion is supported by the development and demonstration of a growing array of attacks on certain types of protections biomedical data managers aim to inject into clinical and genomic data shared for various purposes, such as the obfuscation of a patient’s identity or the suppression of sensitive facts about a research participant or academic medical center. At the same time, these methodologies make strong assumptions about the extent to which an adversary functions in the world, such as operating under no (or limited) constraints with respect to resources at their disposal and motivation for mounting an attack. In this seminar, I will review various attacks on biomedical data as they have evolved over the past several decades, but then posit a new approach to assessing data privacy risk in the real world. This approach builds on computational economic perspectives of risk assessment, with a particular focus on the potential for applying game theory to model pragmatic adversarial settings prior to making decisions about when and how to share data. To illustrate the potential for this approach, I will draw upon several examples of how we have applied it with respect to sharing demographic, clinical, and genomic data, both at the individual- and summary-level for several U.S.-based consortia (including the NIH’s All of Us Research Program) and multinational clinical trials..

MRBIII, Room 1220
16 October 2019
1:30pm