PRISM: privacy-preserving rare disease analysis using fully homomorphic encryption.

IF 5.4
Güliz Akkaya, Nesli Erdoğmuş, Mete Akgün
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引用次数: 0

Abstract

Motivation: Rare diseases affect millions of people worldwide, yet their genomic foundations remain poorly understood due to limited patient data and strict privacy regulations, such as the General Data Protection Regulation (GDPR) (https://gdpr.eu/tag/gdpr/) in March 2025. These restrictions can hinder the collaborative analysis of genomic data necessary for uncovering disease-causing variants.

Results: We present PRISM, a novel privacy-preserving framework based on fully homomorphic encryption (FHE) that facilitates rare disease variant analysis across multiple institutions without exposing sensitive genomic information. To address the challenges of centralized trust, PRISM is built upon a Threshold FHE scheme. This approach decentralizes key management across participating institutions and ensures no single entity can unilaterally decrypt sensitive data. Our method filters disease-causing variants under recessive, dominant, and de novo inheritance models entirely on encrypted data. We propose two algorithmic variants: a multiplication-intensive (MUL-IN) approach and an addition-intensive (ADD-IN) approach. The ADD-IN algorithms minimize the number of costly multiplication operations, enabling up to a 17× improvement in runtime for recessive/dominant filtering and 22× for de novo filtering, compared to MUL-IN methods. While ADD-IN produces larger ciphertexts, efficient parallelization via SIMD and multithreading allows it to handle millions of variants in reasonable time. To the best of our knowledge, this is the first study that utilizes FHE for privacy-preserving rare disease analysis across multiple inheritance models, demonstrating its practicality and scalability in a single-cloud setting.

Availability and implementation: The source code and the data used in this work can be found in https://github.com/mdppml/PRISM.git.

PRISM:使用完全同态加密保护隐私的罕见疾病分析。
动机:罕见疾病影响着全球数百万人,但由于患者数据有限和严格的隐私法规(如2025年3月的《通用数据保护条例》(GDPR) (https://gdpr.eu/tag/gdpr/)),人们对罕见疾病的基因组基础知之甚少。这些限制可能阻碍揭示致病变异所必需的基因组数据的协作分析。结果:我们提出了PRISM,这是一个基于完全同态加密(FHE)的新型隐私保护框架,可以在不暴露敏感基因组信息的情况下促进跨多个机构的罕见疾病变异分析。为了解决中心化信任的挑战,PRISM建立在阈值FHE方案之上。这种方法分散了参与机构之间的密钥管理,并确保没有任何一个实体可以单方面解密敏感数据。我们的方法完全在加密数据上过滤隐性、显性和新生遗传模型下的致病变异。我们提出了两种算法变体:乘法密集型(mulin)方法和加法密集型(ADD-IN)方法。与mulin方法相比,ADD-IN算法最大限度地减少了代价高昂的乘法运算次数,使隐性/显性滤波的运行时间提高了17倍,从头滤波的运行时间提高了22倍。虽然ADD-IN产生更大的密文,但通过SIMD和多线程进行的高效并行化使它能够在合理的时间内处理数百万个变体。据我们所知,这是第一个利用FHE在多个遗传模型中进行保护隐私的罕见病分析的研究,证明了其在单云环境中的实用性和可扩展性。可用性:本工作中使用的源代码和数据可在https://github.com/mdppml/PRISM.git.Supplementary信息中找到:补充数据可在Bioinformatics在线上找到。
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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