用于比较压缩DNA序列的隐私增强方法
Privacy-Enhanced Methods for Comparing Compressed DNA Sequences
摘要：
在本文中，我们研究的方法，以提高效率和隐私的压缩DNA序列比较计算，在各种查询场景。例如，一个场景涉及查询者Bob，其想要测试他的DNA字符串$Q$是否接近于数据所有者Alice所拥有的DNA字符串$Y$，但是Bob不想向Alice揭示$Q$，并且Alice愿意向Bob揭示$Y$，因为$Q$接近于$Q$。我们描述了一种隐私增强的方法，用于比较两个压缩的DNA序列，它可以用来实现这样一个场景的目标。我们的方法涉及到一个减少设置差异，我们描述了一个隐私增强协议设置差异，实现绝对隐私的鲍勃（在信息理论意义上），和量化程度的隐私保护爱丽丝。我们的协议，这使得他们非常适合隐私增强的DNA序列比较问题的一个重要特征是，我们的解决方案的通信复杂性是成比例的阈值，限制的基数集的差异，是感兴趣的，而不是基数集的参与（这与DNA序列的长度）。此外，在我们的协议中，查询器，鲍勃，可以很容易地计算集差，只有当它的基数接近或低于指定的阈值。
英文摘要：
In this paper, we study methods for improving the efficiency and privacy of compressed DNA sequence comparison computations, under various querying scenarios. For instance, one scenario involves a querier, Bob, who wants to test if his DNA string, $Q$, is close to a DNA string, $Y$, owned by a data owner, Alice, but Bob does not want to reveal $Q$ to Alice and Alice is willing to reveal $Y$ to Bob \emph{only if} it is close to $Q$. We describe a privacy-enhanced method for comparing two compressed DNA sequences, which can be used to achieve the goals of such a scenario. Our method involves a reduction to set differencing, and we describe a privacy-enhanced protocol for set differencing that achieves absolute privacy for Bob (in the information theoretic sense), and a quantifiable degree of privacy protection for Alice. One of the important features of our protocols, which makes them ideally suited to privacy-enhanced DNA sequence comparison problems, is that the communication complexity of our solutions is proportional to a threshold that bounds the cardinality of the set differences that are of interest, rather than the cardinality of the sets involved (which correlates to the length of the DNA sequences). Moreover, in our protocols, the querier, Bob, can easily compute the set difference only if its cardinality is close to or below a specified threshold.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
