Secure Comparisons of Single Nucleotide Polymorphisms Using Secure Multiparty Computation: Method Development

TL;DR

This paper introduces a secure method for comparing genetic data using privacy-preserving computation to protect individual genomic information.

Contribution

The paper presents efficient secure multiparty computation protocols for SNP set operations and similarity metrics.

Findings

Secure computation of SNP set operations like union and intersection is feasible for large datasets.

Jaccard similarity between two genomes with 400,000 SNPs can be computed in under 2.16 seconds under malicious adversary assumptions.

The methods are practical for real-world genomic data and support multiple security models.

Abstract

While genomic variations can provide valuable information for health care and ancestry, the privacy of individual genomic data must be protected. Thus, a secure environment is desirable for a human DNA database such that the total data are queryable but not directly accessible to involved parties (eg, data hosts and hospitals) and that the query results are learned only by the user or authorized party.

In this study, we provide efficient and secure computations on panels of single nucleotide polymorphisms (SNPs) from genomic sequences as computed under the following set operations: union, intersection, set difference, and symmetric difference.

Using these operations, we can compute similarity metrics, such as the Jaccard similarity, which could allow querying a DNA database to find the same person and genetic relatives securely. We analyzed various security paradigms and show metrics for the protocols under several security assumptions, such as semihonest, malicious with honest majority, and malicious with a malicious majority.

We show that our methods can be used practically on realistically sized data. Specifically, we can compute the Jaccard similarity of two genomes when considering sets of SNPs, each with 400,000 SNPs, in 2.16 seconds with the assumption of a malicious adversary in an honest majority and 0.36 seconds under a semihonest model.

Our methods may help adopt trusted environments for hosting individual genomic data with end-to-end data security.

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