Protecting Genomic Privacy by a Sequence-Similarity Based Obfuscation Method

TL;DR

This paper introduces IterMegaBLAST, a fast and accurate sequence similarity-based obfuscation method that enhances genomic privacy protection while maintaining utility, outperforming existing methods in efficiency and accuracy.

Contribution

The paper presents a novel sequence similarity-based obfuscation method, IterMegaBLAST, which improves privacy protection in genomic data with higher efficiency and accuracy than prior approaches.

Findings

IterMegaBLAST significantly outperforms existing methods in utility accuracy.

It reduces time complexity for genomic privacy protection.

The method effectively balances privacy and data utility.

Abstract

In the post-genomic era, large-scale personal DNA sequences are produced and collected for genetic medical diagnoses and new drug discovery, which, however, simultaneously poses serious challenges to the protection of personal genomic privacy. Existing genomic privacy-protection methods are either time-consuming or with low accuracy. To tackle these problems, this paper proposes a sequence similarity-based obfuscation method, namely IterMegaBLAST, for fast and reliable protection of personal genomic privacy. Specifically, given a randomly selected sequence from a dataset of DNA sequences, we first use MegaBLAST to find its most similar sequence from the dataset. These two aligned sequences form a cluster, for which an obfuscated sequence was generated via a DNA generalization lattice scheme. These procedures are iteratively performed until all of the sequences in the dataset are clustered and their obfuscated sequences are generated. Experimental results on two benchmark datasets demonstrate that under the same degree of anonymity, IterMegaBLAST significantly outperforms existing state-of-the-art approaches in terms of both utility accuracy and time complexity.