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: 10540473
• 负责人: Gamze Gursoy
• 金额: $ 24.9万
• 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/10540473
• 关键词: Assessment tool; Biochemistry; Biomedical Research; Biophysics; Blood; Computational Technique; Consent; Coupled; DNA; Data; Data Science; Data Set; Databases; Detection; Disease; Equilibrium; Extravasation; Future; Gene Frequency; Genetic; Genome; Genomics; Genotype; Goals; Health; Human; Individual; Information Theory; 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; base; 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; 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 However, privacy studies inferring study participations is outdated compared to the pace of the technological advancements in genome sequencing.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 随着大规模基因组数据的激增，不同基因组数据的广度和深度都有了极大的增加 基因组数据集以及在基因组数据科学中个人隐私这一主题中日益重要的问题。 疾病和疾病的详细遗传和环境特征依赖于大规模 挖掘基因-表型关系；因此，人们非常希望尽可能广泛地共享数据。 美国国立卫生研究院最近改变了共享基因组摘要结果的政策，这是向制作数据迈出的一大步 可供更广泛的研究人员使用。然而，与隐私研究相比，推断研究参与的隐私研究是过时的 基因组测序技术进步的步伐。减少私营化的关键第一步 信息泄露是衡量信息泄露的量，特别是在不同的场景下。至 为此，我们建议从信息论的角度对不同类型的私人信息泄露行为进行分析。 基因组数据共享场景，特别是当数据集有噪声和不完整时。我们还将发展 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的敏感信息泄漏的统计指标 以及在基因数据不完善的情况下。我们将系统地分析 患者参与研究的推断风险。其次，我们将设计一种可信的隐私攻击 通过一项实验研究，其中将使用不同的技术来从痕迹中对基因组进行排序 采样量，如触摸对象或用过的眼镜。这将使我们能够研究 秘密DNA检测及其对基因组数据共享的影响。第三，开发风险评估工具 用于共享基因组摘要结果。这些工具将模拟通过以下方式学习的数百种场景 AIM 1中的模拟和AIM 2中的真实隐私攻击，以在数据发布之前量化风险。 这些工具将使用加密技术来实施，以进一步减少私人信息 风险评估步骤中的泄漏。 在K99阶段，该项目的目标是找到所需的最低数量的基因分型信息和 使用模拟和湿实验室检测混合物中的基因组所允许的最大噪声量 实验。为了实现这一研究目标，K99阶段将包括分子生物学方面的培训， 基因组学和隐私。这项培训将在耶鲁大学分子生物物理系进行 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赛车训练的基础上，R00阶段的目标将是模拟 增加样本量和构建隐私风险评估工具的实验培训的结果 从实验和模拟中获得的结果并使用密码学实现了这类工具 技巧。