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 However, privacy studies inferring study participations is outdated compared to the pace of the technological advancements in genome sequencing.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 随着大型基因组数据的激增，不同的宽度和深度大大增加 基因组学数据集以及基因组数据科学中个人隐私的主题的重要性越来越重要。 疾病和状况的详细遗传和环境表征取决于大规模 挖掘基因型 - 表型关系；因此，人们渴望尽可能广泛地共享数据。 共享基因组摘要结果的NIH政策的最新变化是制定数据的重要一步 可用于更广泛的研究人员。但是，将研究参与参与的隐私研究比较 达到基因组测序中技术进步的速度。减少私人的关键第一步 信息泄漏是为了衡量信息泄漏的量，尤其是在不同情况下。到 我们建议在此目的中得出信息 - 用于不同的私人信息泄漏的理论措施 基因组数据共享方案，尤其是当数据集嘈杂且不完整时。我们还将发展 各种风险评估工具。我们将根据三个目标进行隐私分析。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 场景以及基因型数据不完美的条件。我们将系统地分析 患者研究参与的推理风险。第二，我们将设计一个合理的隐私攻击 通过一项实验研究，其中将使用不同的技术来对痕量序列进行测序 诸如触摸物体或二手眼镜之类的样品数量。这将使我们能够研究合理的情况 秘密的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 用于共享基因组摘要结果。这些工具将模拟数百种通过 AIM 1中的仿真和现实生活中的隐私攻击中的AIM 2中的模拟在发布数据之前量化风险。 这些工具将使用加密技术实施，以进一步减少私人信息 风险评估步骤中的泄漏。 在K99阶段，该项目的目的是找到所需的最少基因分型信息和 使用模拟和湿lab在混合物中检测基因组的最大噪声量 实验。为了实现这一研究目标，K99阶段将涉及分子生物学的培训， 基因组学和隐私。该培训将在分子生物物理学系的耶鲁大学举行 和生物化学，在马克·格斯坦（Mark Gerstein）博士（基因组学和隐私）和安德鲁·米兰克（Andrew Miranker）的指导下 （分子生物学）。在K99期间训练的基础上，R00阶段的目标将是模拟 实验培训的结果，以增加样本量和建立隐私风险评估工具 从实验和模拟中学到的结果以及使用加密的实施此类工具 技术。