Yilmaz Emre, Ji Tianxi, Ayday Erman, Li Pan
University of Houston-Downtown, Houston, Texas.
Case Western Reserve University, Cleveland, Ohio.
CODASPY. 2022 Apr;2022:77-88. doi: 10.1145/3508398.3511519. Epub 2022 Apr 15.
Privacy-preserving genomic data sharing is prominent to increase the pace of genomic research, and hence to pave the way towards personalized genomic medicine. In this paper, we introduce (, )-dependent local differential privacy (LDP) for privacy-preserving sharing of correlated data and propose a genomic data sharing mechanism under this privacy definition. We first show that the original definition of LDP is not suitable for genomic data sharing, and then we propose a new mechanism to share genomic data. The proposed mechanism considers the correlations in data during data sharing, eliminates statistically unlikely data values beforehand, and adjusts the probability distributions for each shared data point accordingly. By doing so, we show that we can avoid an attacker from inferring the correct values of the shared data points by utilizing the correlations in the data. By adjusting the probability distributions of the shared states of each data point, we also improve the utility of shared data for the data collector. Furthermore, we develop a greedy algorithm that strategically identifies the processing order of the shared data points with the aim of maximizing the utility of the shared data. Our evaluation results on a real-life genomic dataset show the superiority of the proposed mechanism compared to the randomized response mechanism (a widely used technique to achieve LDP).
隐私保护的基因组数据共享对于加快基因组研究步伐、从而为个性化基因组医学铺平道路至关重要。在本文中，我们引入用于相关数据隐私保护共享的（，）相关局部差分隐私（LDP），并在此隐私定义下提出一种基因组数据共享机制。我们首先表明LDP的原始定义不适用于基因组数据共享，然后提出一种新的基因组数据共享机制。所提出的机制在数据共享期间考虑数据中的相关性，预先消除统计上不太可能的数据值，并相应地调整每个共享数据点的概率分布。通过这样做，我们表明可以避免攻击者通过利用数据中的相关性推断共享数据点的正确值。通过调整每个数据点共享状态的概率分布，我们还提高了数据收集器共享数据的效用。此外，我们开发了一种贪心算法，该算法以最大化共享数据的效用为目标，策略性地确定共享数据点的处理顺序。我们在真实基因组数据集上的评估结果表明，与随机响应机制（一种广泛用于实现LDP的技术）相比，所提出的机制具有优越性。
