Privately computing set-maximal matches in genomic data
Privately computing set-maximal matches in genomic data
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DOI：
10.1186/s12920-020-0718-x
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发表时间：
2020-07-21
影响因子：
2.7
通讯作者：
Chen, Hao
中科院分区：
文献类型：
--
作者：
Sotiraki, Katerina;Ghosh, Esha;Chen, Hao
背景在大型比对基因序列中寻找脱氧核糖核酸（DNA）序列中的长匹配是一个非常感兴趣的问题。一个典型的应用是通过DNA数据中的大的共同序列来识别远亲。然而，由于基因组数据的敏感性，这种没有安全考虑的计算可能会损害所涉及的个人的隐私。方法秘密共享技术使匹配的计算，同时尊重参与方的输入的隐私。这种方法需要依赖于计算所需的电路深度的交互。结果我们设计了一种新的深度优化算法，用于计算比对基因序列数据库与个体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，我们执行并评估我们的协议。结论使用现代加密技术，以保护隐私的方式执行困难的基因组计算。我们丰富了这一研究领域，提出了一个隐私保护协议集最大匹配。
Background 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. Methods 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. Results 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. Conclusions 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.