A Risk Management Framework for Identifiability in Genomics Research

基因组学研究中可识别性的风险管理框架

• 批准号: 9360125
• 负责人: Bradley A. Malin
• 金额: $ 24.96万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-21 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9360125
• 关键词: Academic Medical Centers; Address; Adopted; Agreement; Back; Behavior; Case Study; Collection; Computerized Medical Record; Contracts; Control Groups; Cost Measures; Data; Data Aggregation; Data Collection; Data Security; Data Set; Databases; Detection; Foundations; Funding; Genome; Genomic Segment; Genomic approach; Genomics; Grant; Health; Individual; Information Systems; Internet; Investigation; Knowledge; Lead; Length; Life; Link; Measures; Medical center; Modeling; Motivation; Names; Paper; Participant; Peer Review; Phase; Play; Policies; Policy Maker; Precision Medicine Initiative; Privacy; Probability; Process; Progress Reports; Publications; Published Database; Publishing; Punishment; Records; Reporting; Research; Research Personnel; Research Project Grants; Resources; Risk; Risk Assessment; Risk Management; Scientist; Services; Societies; System; Theoretical model; Time; Translations; United States National Institutes of Health; Variant; Work; base; cohort; cost; data sharing; database of Genotypes and Phenotypes; design; genomic data; human subject; phenome; phenotypic data; precision medicine; repository; risk mitigation; social; socioeconomics; statistics; theories; tool; virtual

The past decade has witnessed numerous demonstrations that genomic data can be traced back to the corresponding named individuals. These attacks exploit various collections, including the NIH Database of Genotypes and Phenotypes (dbGaP), the 1000 Genomes Project, and the Beacon Project of the Global Alliance for Genomics and Health, and are often reported in the popular media. At the same time, research conducted in the first phase of this grant (from 2012-2016) showed that such re-identification attacks often represent worst- case, non-generalizable scenarios. Specifically, it was shown that these attacks often focus on the possibility of attack - and not its probability given the wide range of factors often at play in practice. By focusing on the possible, such investigations can lead policy makers to believe that de-identification is a useless activity. However, our research showed that de-identification is only one part of a larger strategy of deterrents that can be used to manage risk. By intelligently combining de-identification with other technical risk mitigation approaches (e.g., controlled access) and societal constructs (e.g., data use agreements and penalties), genomic data sharing solutions can be developed with appropriate levels of risk and utility for scientists and society. While our research laid the foundation for managing identification risk in genomic data sharing, significant questions remain regarding its translation into practical guidance. In particular, risk management models must be specialized to the type of data that is shared, the types of penalties (or punishments) available, and the costs of adopting and administering deterrence mechanisms. Thus, in the second phase of this research project, we propose to augment risk-based re-identification management frameworks to model and assess the deterrence approaches invoked by existing repositories, such as dbGaP (which holds a collection of smaller historical datasets from completed studies), as well as emerging initiatives, such as the Precision Medicine Initiative. This project will pursue three specific aims, designed to work in harmony, but at the same time sufficiently independent that if one fails, the research will still yield fruitful risk management guidance for genomic databases: 1) Develop game theoretic models to assess re-identification attacks at different levels of detail in genomic data sharing (e.g., aggregate summaries of the proportion of variants in case vs. control groups in association studies); 2) Characterize and measure the costs associated with common re-identification deterrence approaches for genomic data (e.g., physical investigatory reviews and virtual audits of IT system use); and 3) Optimize the parameterization of a deterrence policy (e.g., the amount of damages for violation of a data use agreement or the amount of time to withhold data from an attacker/investigator) given the expected value of genomic data. We will evaluate these approaches with a large repository of de-identified genomic and electronic medical records in use at a large academic medical center, datasets hosted at two federal repositories, and a web system that presents summary statistics from a cohort of 9000 participants.

过去的十年见证了许多证明基因组数据可以追溯到