Enabling Trade-offs in Privacy and Utility in Genomic Data Beacons and Summary Statistics

TL;DR

This paper presents optimization-based methods to balance privacy and utility in genomic data sharing, effectively reducing privacy risks while maintaining data usefulness in summary statistics and Beacon responses.

Contribution

It introduces scalable optimization techniques for privacy-utility tradeoffs in genomic data sharing, considering multiple attack models and outperforming existing methods.

Findings

Outperforms state-of-the-art in privacy and utility balance

Effectively mitigates likelihood-ratio-based membership inference attacks

Provides scalable solutions for large genomic datasets

Abstract

The collection and sharing of genomic data are becoming increasingly commonplace in research, clinical, and direct-to-consumer settings. The computational protocols typically adopted to protect individual privacy include sharing summary statistics, such as allele frequencies, or limiting query responses to the presence/absence of alleles of interest using web-services called Beacons. However, even such limited releases are susceptible to likelihood-ratio-based membership-inference attacks. Several approaches have been proposed to preserve privacy, which either suppress a subset of genomic variants or modify query responses for specific variants (e.g., adding noise, as in differential privacy). However, many of these approaches result in a significant utility loss, either suppressing many variants or adding a substantial amount of noise. In this paper, we introduce optimization-based approaches to explicitly trade off the utility of summary data or Beacon responses and privacy with respect to membership-inference attacks based on likelihood-ratios, combining variant suppression and modification. We consider two attack models. In the first, an attacker applies a likelihood-ratio test to make membership-inference claims. In the second model, an attacker uses a threshold that accounts for the effect of the data release on the separation in scores between individuals in the dataset and those who are not. We further introduce highly scalable approaches for approximately solving the privacy-utility tradeoff problem when information is either in the form of summary statistics or presence/absence queries. Finally, we show that the proposed approaches outperform the state of the art in both utility and privacy through an extensive evaluation with public datasets.