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

arXiv: https://arxiv.org/abs/2302.01763 · HTML: https://mlsanity.com/paper/2302.01763/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 11 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Collecting and Analyzing Multidimensional Data with Local Differential Privacy](https://mlsanity.com/paper/1907.00782/) (2019)
- [P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model](https://mlsanity.com/paper/2006.12101/) (2020)
- [Re-identification of Individuals in Genomic Datasets Using Public Face Images](https://mlsanity.com/paper/2102.08557/) (2021)
- [Genome Reconstruction Attacks Against Genomic Data-Sharing Beacons](https://mlsanity.com/paper/2001.08852/) (2020)
- [Defending Against Membership Inference Attacks on Beacon Services](https://mlsanity.com/paper/2112.13301/) (2021)
- [Providing Input-Discriminative Protection for Local Differential Privacy](https://mlsanity.com/paper/1911.01402/) (2019)

## Built on by (most cited first, 1 of 1)

- [Bayes-Nash Generative Privacy Against Membership Inference Attacks](https://mlsanity.com/paper/2410.07414/) (2024)

## Abstract (arXiv)

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.