Finding long matches in deoxyribonucleic acid (DNA) sequences in large aligned genetic sequences is a problem of great interest. A paradigmatic application is the identification of distant relatives via large common subsequences in DNA data. However, because of the sensitive nature of genomic data such computations without security consideration might compromise the privacy of the individuals involved. The secret sharing technique enables the computation of matches while respecting the privacy of the inputs of the parties involved. This method requires interaction that depends on the circuit depth needed for the computation. We design a new depth-optimized algorithm for computing set-maximal matches between a database of aligned genetic sequences and the DNA of an individual while respecting the privacy of both the database owner and the individual. We then implement and evaluate our protocol. Using modern cryptographic techniques, difficult genomic computations are performed in a privacy-preserving way. We enrich this research area by proposing a privacy-preserving protocol for set-maximal matches.

中文翻译：

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私下计算基因组数据中的集合最大匹配。

在大型比对的遗传序列中找到脱氧核糖核酸（DNA）序列的长匹配是一个非常令人感兴趣的问题。一种典型的应用是通过DNA数据中的大量常见子序列来识别远亲。但是，由于基因组数据的敏感性，这种不考虑安全性的计算可能会损害所涉及个人的隐私。秘密共享技术可以在确保所涉及各方的输入隐私的同时进行匹配计算。此方法需要进行交互，该交互取决于计算所需的电路深度。我们设计了一种新的深度优化算法，用于计算比对的遗传序列数据库与个体DNA之间的集合最大匹配，同时尊重数据库所有者和个体的隐私。然后，我们实施并评估我们的协议。使用现代密码技术，以保护隐私的方式执行困难的基因组计算。我们为最大匹配集提出了一种隐私保护协议，从而丰富了这一研究领域。