SAFEGENOMES: Strong privacy Assurance For Effective GENOME Sharing

SAFEGENOMES：强大的隐私保证，有效实现基因组共享

基本信息

批准号：
10532442
负责人：
Luca Bonomi
金额：
$ 24.9万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-14 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10532442
关键词：
Address Appointment Biomedical Research Collaborations Complement Computational Biology Custom Data Data Analytics Data Collection Data Correlations Data Reporting Databases Development Plans Disclosure Ensure Equilibrium Excision Eye Faculty Gender Genome Genomic medicine Genomics Genotype Goals Hair Color Health Health Promotion Human Individual Institution Knowledge Lead Medical Research Methods Mission Modeling National Human Genome Research Institute Noise Outcome Participant Phase Phenotype Policies Privacy Privatization Publications Publishing Records Reproducibility Research Research Personnel Risk Sampling Techniques Technology Training Activity Trust Uncertainty Universities Variant Work base biomedical informatics burden of illness career career development computer science cryptography data access data anonymization data de-identification data preservation data privacy data sharing design experience flexibility genetic variant genome wide association study genomic data improved personalized medicine precision medicine prevent privacy preservation privacy protection programs skills statistics usability

项目摘要

Project Summary Genomic data are vital for advancing medical research and achieving breakthroughs. However, disclosure of genomic data has serious privacy implications that can lead to a loss of trust from data contributors and restricting researchers’ access to data. To facilitate data-driven genomic research, it is crucial to address the privacy risks in data sharing and to develop privacy-preserving solutions to protect study participants. This project will study the privacy risks in realistic attack models and develop privacy methods that balance individual privacy and the utility of shared data. Overall, the proposed solutions will enable institutions to share high utility data while providing strong privacy assurance to data contributors, facilitating data collection and improving data usability. In the first aim, a privacy-preserving data publication framework will be developed to “safely anonymize” genomic data and optimize the released data toward application needs. The framework will protect individuals from re- identification and also prevent inference attacks that may be conducted using publicly available phenotypes (e.g., eye/hair color). In the second aim, customizable privacy solutions will be developed against realistic adversarial models when data statistics are released. Building on recent privacy models, the proposed solutions will account for the adversary's external knowledge and customizable sensitive information to effectively strike a balance between privacy and utility, improving data usability compared to standard differential privacy models. This project will advance current solutions for genomic data anonymization and improve the usability of differential privacy and its variants, with the goal of facilitating highly usable and privacy-preserving data sharing. This work will widen the access to genomic data, promote transparency, and facilitate reproducibility for genomic applications. This project is in line with the mission of the National Human Genome Research Institute (NHGRI), as the proposed techniques enhance data sharing and promote collaborative genomic research. The applicant’s career goal is to become an independent investigator with a primary appointment in a biomedical informatics program, with a focus on genome privacy technologies, at a major US research university. His long- term objective is to develop new privacy-preserving technologies for data sharing and data analytics, in order to facilitate collaborative research efforts in genomics and precision medicine. The applicant proposes a carefully designed career development plan, which includes a variety of training activities to complement his computer science skills with additional biomedical knowledge and smooth his transition into an independent researcher. The UCSD Health Department of Biomedical Informatics will serve as an exceptional platform for his career development, given the experience of several faculty in privacy technologies, computational biology, genomic medicine, and close collaboration with other institutions worldwide.

项目概要基因组数据对于推进医学研究和实现突破至关重要。基因组数据具有严重的隐私影响，可能导致数据贡献者失去信任并限制为了促进数据驱动的基因组研究，解决隐私风险至关重要。数据共享并开发隐私保护解决方案来保护研究。现实攻击模型中的隐私风险，并开发平衡个人隐私和总体而言，所提出的解决方案将使机构能够共享高效用数据。为数据贡献者提供强有力的隐私保证，促进数据收集并提高数据可用性。第一个目标是开发一个保护隐私的数据发布框架，以“安全地匿名”基因组数据并根据应用程序需求优化发布的数据，该框架将保护个人免遭重新发布。识别并防止可能使用公开可用的表型进行的推理攻击（例如，第二个目标是针对现实的对抗开发可定制的隐私解决方案。数据统计发布时的模型基于最近的隐私模型，提出的解决方案将考虑在内。为对手的外部知识和可定制的敏感信息有效地取得平衡隐私和实用性之间的平衡，与标准差分隐私模型相比，提高了数据可用性。该项目将推进当前基因组数据匿名化的解决方案，并提高差异化的可用性隐私及其变体，旨在促进高度可用且保护隐私的数据共享。将扩大基因组数据的获取范围，提高透明度并促进基因组的可重复性该项目符合国家人类基因组研究所（NHGRI）的使命，因为所提出的技术增强了数据共享并促进了协作基因组研究。申请人的职业目标是成为一名独立调查员，并在生物医学领域担任主要职务他在美国一所主要研究型大学从事信息学项目，重点关注基因组隐私技术。长期目标是开发用于数据共享和数据分析的新隐私保护技术，以便促进基因组学和精准医学方面的合作研究工作申请人提出了谨慎的建议。设计职业发展计划，其中包括各种培训活动以补充他的计算机科学技能和额外的生物医学知识，并顺利过渡为独立研究员。加州大学圣地亚哥分校健康生物医学信息学系将为他的职业生涯提供一个特殊的平台鉴于隐私技术、计算生物学、基因组学方面的几位教师的经验，开发医学，以及与世界各地其他机构的密切合作。