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Motivation: Researchers need a rich trove of genomic datasets that they canleverage to gain a better understanding of the genetic basis of the humangenome and identify associations between phenotypes and specific parts of DNA.However, sharing genomic datasets that include sensitive genetic or medicalinformation of individuals can lead to serious privacy-related consequences ifdata lands in the wrong hands. Restricting access to genomic datasets is onesolution, but this greatly reduces their usefulness for research purposes. Toallow sharing of genomic datasets while addressing these privacy concerns,several studies propose privacy-preserving mechanisms for data sharing.Differential privacy (DP) is one of such mechanisms that formalize rigorousmathematical foundations to provide privacy guarantees while sharing aggregatedstatistical information about a dataset. However, it has been shown that theoriginal privacy guarantees of DP-based solutions degrade when there aredependent tuples in the dataset, which is a common scenario for genomicdatasets (due to the existence of family members). Results: In this work, weintroduce a near-optimal mechanism to mitigate the vulnerabilities of theinference attacks on differentially private query results from genomic datasetsincluding dependent tuples. We propose a utility-maximizing andprivacy-preserving approach for sharing statistics by hiding selective SNPs ofthe family members as they participate in a genomic dataset. By evaluating ourmechanism on a real-world genomic dataset, we empirically demonstrate that ourproposed mechanism can achieve up to 40% better privacy than state-of-the-artDP-based solutions, while near-optimally minimizing the utility loss.

Near-Optimal Privacy-Utility Tradeoff in Genomic Studies Using Selective SNP Hiding