Re-identification of Individuals in Genomic Datasets Using Public Face Images

TL;DR

This paper assesses the real-world risk of re-identifying individuals in genomic datasets using public face images, finding the actual threat is lower than previously thought and proposing noise addition as a mitigation strategy.

Contribution

It provides a systematic analysis of re-identification risks with real images and introduces a method to reduce this risk through imperceptible noise addition.

Findings

Re-identification success is lower with real images than prior studies suggested.

Adding small, carefully crafted noise reduces re-identification risk significantly.

The proposed noise method maintains image quality while protecting privacy.

Abstract

DNA sequencing is becoming increasingly commonplace, both in medical and direct-to-consumer settings. To promote discovery, collected genomic data is often de-identified and shared, either in public repositories, such as OpenSNP, or with researchers through access-controlled repositories. However, recent studies have suggested that genomic data can be effectively matched to high-resolution three-dimensional face images, which raises a concern that the increasingly ubiquitous public face images can be linked to shared genomic data, thereby re-identifying individuals in the genomic data. While these investigations illustrate the possibility of such an attack, they assume that those performing the linkage have access to extremely well-curated data. Given that this is unlikely to be the case in practice, it calls into question the pragmatic nature of the attack. As such, we systematically study this re-identification risk from two perspectives: first, we investigate how successful such linkage attacks can be when real face images are used, and second, we consider how we can empower individuals to have better control over the associated re-identification risk. We observe that the true risk of re-identification is likely substantially smaller for most individuals than prior literature suggests. In addition, we demonstrate that the addition of a small amount of carefully crafted noise to images can enable a controlled trade-off between re-identification success and the quality of shared images, with risk typically significantly lowered even with noise that is imperceptible to humans.