Privacy with Good Taste: A Case Study in Quantifying Privacy Risks in Genetic Scores
Abstract
Analysis of genetic data opens up many opportunities for medical and scientific advances. The use of phenotypic information and polygenic risk scores to analyze genetic data is widespread. Most work on genetic privacy focuses on basic genetic data such as SNP values and specific genotypes. In this talk, I will present a novel methodology to quantify and prevent privacy risks by focusing on polygenic scores and phenotypic information. The methodology is based on the tool-supported privacy risk analysis method Privug. I will show the use of Privug to assess privacy risks posed by disclosing a polygenic trait score for the bitter taste receptors, TAS2R38 and TAS2R16, to a person’s privacy in regards to their ethnicity. I will describe the privacy risks analysis of different programs for genetic data disclosure: taster phenotype, tasting polygenic score, and a polygenic score distorted with noise. Finally, I will discuss the privacy/utility trade-offs of the tasting polygenic score.
Raúl Pardo is an assistant professor in computer science at the IT University of Copenhagen. In the past, he was a postdoc in the SQUARE group at the IT University of Copenhagen hosted by Andrzej Wąsowski. Previously, he was a postdoc in the Privatics team at Inria hosted by Daniel Le Métayer. He has a PhD degree in computer science from Chalmers University of Technology.