利用信息论隐私隐藏敏感基因型的机制
Mechanisms for Hiding Sensitive Genotypes with Information-Theoretic Privacy
摘要：
受个人基因组学服务日益可用性的启发，我们研究了共享基因组数据时出现的信息理论隐私问题：用户希望共享他或她的基因组序列，同时将基因型隐藏在某些位置，否则可能会泄露关键的健康相关信息。擦除（掩蔽）所选基因型的直接解决方案不能确保隐私，因为附近位置之间的相关性可能泄露掩蔽的基因型。我们引入了一个基于擦除的隐私机制与完善的信息理论的隐私，从而释放的序列是统计独立的敏感基因型。我们的机制可以被解释为一个局部最优的贪心算法的序列位置的给定的处理顺序，其中的效用是由没有擦除释放的位置的数量来衡量。我们发现，找到一个最佳顺序是NP-困难的一般，并提供了一个上限的最佳效用。对于遗传学中的标准建模方法隐马尔可夫模型中的序列，我们提出了一种有效的算法实现我们的机制，序列长度的复杂性多项式。此外，我们说明了该机制的鲁棒性，从错误的先验分布的隐私泄漏的边界。我们的工作是朝着更严格控制基因组数据共享中的隐私迈出的一步。
英文摘要：
Motivated by the growing availability of personal genomics services, we study an information-theoretic privacy problem that arises when sharing genomic data: a user wants to share his or her genome sequence while keeping the genotypes at certain positions hidden, which could otherwise reveal critical health-related information. A straightforward solution of erasing (masking) the chosen genotypes does not ensure privacy, because the correlation between nearby positions can leak the masked genotypes. We introduce an erasure-based privacy mechanism with perfect information-theoretic privacy, whereby the released sequence is statistically independent of the sensitive genotypes. Our mechanism can be interpreted as a locally-optimal greedy algorithm for a given processing order of sequence positions, where utility is measured by the number of positions released without erasure. We show that finding an optimal order is NP-hard in general and provide an upper bound on the optimal utility. For sequences from hidden Markov models, a standard modeling approach in genetics, we propose an efficient algorithmic implementation of our mechanism with complexity polynomial in sequence length. Moreover, we illustrate the robustness of the mechanism by bounding the privacy leakage from erroneous prior distributions. Our work is a step towards more rigorous control of privacy in genomic data sharing.
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