Realistic quantification of potential privacy loss from genomic summary results

从基因组摘要结果中实际量化潜在隐私损失

• 批准号: 10616768
• 负责人: Gamze Gursoy
• 金额: $ 23.99万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-09 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10616768
• 关键词: Assessment tool; Binding; Biochemistry; Biomedical Research; Biophysics; Blood; Computational Technique; Consent; Coupled; DNA; Data; Data Science; Data Set; Databases; Democracy; Detection; Disease; Extravasation; Future; Gene Frequency; Genetic; Genome; Genomics; Genotype; Glass; Goals; Health; Human; Individual; Information Theory; Learning; Life; Link; Masks; Measures; Medical Research; Mentorship; Mining; Modeling; Molecular; Molecular Biology; Molecular Biology Techniques; Noise; Participant; Patients; Phase; Phenotype; Policies; Population; Privacy; Privatization; Protocols documentation; Research; Research Personnel; Risk; Risk Assessment; Sample Size; Sampling; Single Nucleotide Polymorphism; Source; Stigmatization; Swab; Techniques; Technology; Testing; Touch sensation; Training; United States National Institutes of Health; Universities; Work; cohort; cryptography; data sharing; design; experimental study; functional genomics; genetic testing; genome sequencing; genomic data; improved; laboratory experiment; nanopore; novel; patient privacy; personalized medicine; point of care; portability; risk prediction; simulation; success; tool; usability

PROJECT ABSTRACT With the surge of large genomics data, there is an immense increase in the breadth and depth of different genomics datasets and an increasing importance in the topic of privacy of individuals in genomic data science. Detailed genetic and environmental characterization of diseases and conditions relies on the large-scale mining of genotype-phenotype relationships; hence, there is great desire to share data as broadly as possible. The recent change in NIH policy of sharing genomic summary results is a great step towards making the data available to broader researchers. However, privacy studies inferring study participations is outdated compared to the pace of the technological advancements in genome sequencing. A key first step in reducing private information leakage is to measure the amount of information leakage, particularly under different scenarios. To this end, we propose to derive information- theoretic measures for private information leakage in different genomic data sharing scenarios, especially when the datasets are noisy and incomplete. We will also develop various risk assessment tools. We will approach the privacy analysis under three aims. First, we will develop statistical metrics that can be used to quantify the sensitive information leakage in different data sharing scenarios as well as under the conditions when the genotype data is imperfect. We will systematically analyze the risk of inference of study participation of a patient. Second, we will design a plausible privacy attack through an experimental study, in which different technologies will be used to sequence genomes from trace amount of samples such as touch objects or used glasses. This will allow us to study the plausible scenarios of surreptious DNA testing and its effect on genomic data sharing. Third, we will develop risk assessment tools for sharing genomic summary results. These tools will simulate hundreds of scenarios learned through simulations in aim 1 and real-life privacy attacks in aim 2 to quantify the risks before the release of the data. These tools will be implemented using cryptographic techniques to further reduce the private information leakage during risk assessment step. During the K99 phase, the aim of this project is to find minimum amount of genotyping information required and maximum amount of noise tolerated for detection of a genome in a mixture using simulations and wet-lab experiments. To accomplish this research goal, the K99 phase will involve training in molecular biology, genomics and privacy. This training will take place at Yale University in the department of Molecular Biophysics and Biochemistry, under the mentorship of Dr. Mark Gerstein (genomics and privacy) and Dr. Andrew Miranker (molecular biology). Building on the training during the K99, the goal of the R00 phase will be simulation of the results of the experimental training to increase the sample size and building privacy risk assessment tools with the results learned from the experiment and simulations and implementation of such tools using cryptographic techniques.

项目摘要 随着大量基因组学数据的激增，不同基因组学的广度和深度都有了巨大的增长。 基因组学数据集和基因组数据科学中个人隐私主题的重要性日益增加。 疾病和病症的详细遗传和环境特征依赖于大规模的 基因型-表型关系的挖掘;因此，非常希望尽可能广泛地共享数据。 NIH最近改变了分享基因组总结结果的政策，这是朝着使数据 提供给更广泛的研究人员。然而，隐私研究推断学习参与是过时的相比， 基因组测序技术进步的步伐。减少私营部门的关键第一步 信息泄漏是衡量信息泄漏的数量，特别是在不同的场景下。到 为此，我们提出了在不同的隐私信息泄漏的信息理论措施， 基因组数据共享场景，特别是当数据集是嘈杂和不完整的。我们还将开发 各种风险评估工具。我们将在三个目标下进行隐私分析。首先，我们将发展 可用于量化不同数据共享中敏感信息泄漏的统计指标 场景以及在基因型数据不完善的条件下。我们将系统分析 推断患者参与研究的风险。第二，我们将设计一个合理的隐私攻击， 通过一项实验性研究，将使用不同的技术对来自微量元素的基因组进行测序， 样本量，如触摸物体或用过的眼镜。这将使我们能够研究 秘密DNA测试及其对基因组数据共享的影响。第三，我们将开发风险评估工具 分享基因组总结结果这些工具将模拟数百个场景， 目标1中的模拟和目标2中的现实隐私攻击，以便在数据发布之前量化风险。 这些工具将使用加密技术来实现，以进一步减少私人信息 风险评估步骤中的泄漏。 在K99阶段，该项目的目的是找到所需的最少量基因分型信息， 使用模拟和湿实验室检测混合物中基因组所能容忍的最大噪声量 实验为了实现这一研究目标，K99阶段将涉及分子生物学方面的培训， 基因组学和隐私这次培训将在耶鲁大学的分子生物物理学系进行 和生物化学，在Mark Gerstein博士（基因组学和隐私）和Andrew Miranker博士的指导下 （分子生物学）。在K99培训的基础上，R 00阶段的目标是模拟 增加样本量和建立隐私风险评估工具的实验培训结果， 从实验和模拟以及使用加密的这种工具的实现中学习到的结果 技术.