Privacy-Preserving Biomedical Database Queries with Optimal Privacy-Utility Trade-Offs

Sharing data across research groups is an essential driver of biomedical research. In particular, biomedical databases with interactive query-answering systems allow users to retrieve information from the database using restricted types of queries. For example, medical data repositories allow researchers designing clinical studies to query how many patients in the database satisfy certain criteria, a workflow known as cohort discovery. In addition, genomic “beacon” services allow users to query whether or not a given genetic variant is observed in the database, a workflow we refer to as variant lookup. While these systems aim to facilitate the sharing of aggregate biomedical insights without divulging sensitive individual-level data, they can still leak private information about the individuals through the query answers. To address these privacy concerns, existing studies have proposed to perturb query results with a small amount of noise in order to reduce sensitivity to underlying individuals [1, 2]. However, these existing efforts either lack rigorous guarantees of privacy or introduce an excessive amount of noise into the system, limiting their effectiveness in practice.

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   https://doi.org/10.1101/2020.01.16.909010.

Authors and Affiliations

1. Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA

   Hyunghoon Cho, Sean Simmons & Bonnie Berger

2. Harvard University, Cambridge, MA, 02138, USA

   Ryan Kim

3. CSAIL, MIT, Cambridge, MA, 02139, USA

   Bonnie Berger

4. Department of Mathematics, MIT, Cambridge, MA, 02139, USA

   Sean Simmons & Bonnie Berger

Corresponding authors

Correspondence to Hyunghoon Cho or Bonnie Berger .

Editors and Affiliations

1. Carnegie Mellon University, Pittsburgh, PA, USA

   Russell Schwartz

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