Privacy-Preserving Genetic Matching Diagnosis on Lightweight Devices

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Xianning Tang, Jichao Xiong, Jiageng Chen, Hui Liu, Heng Xu
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Abstract

Next-generation sequencing (NGS) technology has revolutionized human genome research, significantly advancing the field of genetics. NGS provides unmatched abilities to analyze DNA and RNA molecules efficiently and cost-effectively, transforming genomics research. This technology detects specific alleles within tissues, allowing for accurate diagnoses of genetic mutation-related diseases. However, the sensitivity of genetic information necessitates careful protection across different usage scenarios. In this paper, we introduce an efficient protocol for privacy-preserving genetic matching based on the Private Set Intersection (PSI) technique. Our protocol allows genetic diagnoses without disclosing individual genetic data, offering greater security than previous methods that required external server storage or processing of genetic data. By keeping the data on the individuals local device, we reduce the risks associated with cloud storage. The protocol uses a collision-resistant cuckoo hash table and symmetric encryption methods, ensuring data accuracy and error-free genetic matching diagnoses. Moreover, our protocol is lightweight, utilizing minimal encryption components to maintain security while minimizing computational complexity and client-side load. Experimental results demonstrate that our protocol enhances performance by approximately 31.135% compared to similar protocols on average. These attributes make our PSI-based protocol a robust solution for privacy-preserving genetic matching, safeguarding sensitive genetic information while meeting the efficiency needs of practical applications.

轻量级设备上保护隐私的遗传匹配诊断
下一代测序(NGS)技术使人类基因组研究发生了革命性的变化,极大地推动了遗传学领域的发展。NGS提供了无与伦比的能力来分析DNA和RNA分子高效和经济,改变基因组学研究。这项技术可以检测组织内的特定等位基因,从而准确诊断与基因突变相关的疾病。然而,遗传信息的敏感性需要在不同的使用场景中仔细保护。本文提出了一种基于私有集交集(PSI)技术的高效隐私保护遗传匹配协议。我们的协议允许在不泄露个人遗传数据的情况下进行遗传诊断,比以前需要外部服务器存储或处理遗传数据的方法提供更高的安全性。通过将数据保存在个人本地设备上,我们降低了与云存储相关的风险。该协议采用抗碰撞布谷鸟哈希表和对称加密方法,确保了数据的准确性和无错误的基因匹配诊断。此外,我们的协议是轻量级的,使用最少的加密组件来维护安全性,同时最小化计算复杂性和客户端负载。实验结果表明,与同类协议相比,该协议的性能平均提高了约31.135%。这些特性使我们的基于psi的协议在满足实际应用的效率需求的同时,保护敏感的遗传信息,成为一种具有隐私保护的遗传匹配的鲁棒解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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