Privacy with Good Taste: A Case Study in Quantifying Privacy Risks in Genetic Scores

TL;DR

This paper introduces a new methodology using Privug to quantify and analyze privacy risks associated with sharing polygenic scores and phenotypic data, highlighting privacy concerns in genetic data disclosure.

Contribution

The paper presents a novel privacy risk analysis approach focused on polygenic scores and phenotypic information, extending beyond basic genetic data analysis.

Findings

Polygenic scores for bitter taste receptors can reveal ethnicity.

Different disclosure programs have varying privacy risks.

Adding noise to scores affects privacy and utility trade-offs.

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 paper, we introduce a novel methodology to quantify and prevent privacy risks by focusing on polygenic scores and phenotypic information. Our methodology is based on the tool-supported privacy risk analysis method Privug. We demonstrate the use of Privug to assess privacy risks posed by disclosing a polygenic trait score for bitter taste receptors, encoded by TAS2R38 and TAS2R16, to a person's privacy in regards to their ethnicity. We provide an extensive privacy risks analysis of different programs for genetic data disclosure: taster phenotype, tasting polygenic score, and a polygenic score distorted with noise. Finally, we discuss the privacy/utility trade-offs of the polygenic score.