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

• 批准号: 10551263
• 负责人: Luca Bonomi
• 金额: $ 24.8万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 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/10551263
• 关键词: Address; Appointment; Biomedical Research; Collaborations; Complement; Computational Biology; Data; Data Analytics; Data Collection; Data Correlations; Data Protection; 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; 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; Y Chromosome; biomedical informatics; burden of illness; career; career development; computer science; cryptography; data access; data anonymization; data de-identification; data preservation; data privacy; data reduction; 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

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.

项目摘要 基因组数据对于推进医学研究并取得突破至关重要。但是，披露 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 研究人员访问数据。为了促进数据驱动的基因组研究，应对隐私风险至关重要 在数据共享和开发保护参与者的保护参与者方面。这个项目将研究 现实攻击模型中的隐私风险和开发平衡个人隐私和平衡的隐私方法 共享数据的实用性。总体而言，拟议的解决方案将使机构能够共享高公用事业数据，而 为数据贡献者提供强大的隐私保证，支持数据收集并提高数据可用性。 在第一个目的中，将开发一个保护隐私的数据出版框架来“安全地匿名”基因组 数据并优化已发布的数据满足应用需求。该框架将保护个人免于 识别，还可以防止使用公开表型进行的推理攻击（例如， 眼睛/头发颜色）。在第二个目标中，将针对现实的对抗性开发可自定义的隐私解决方案 释放数据统计数据时的模型。在最近的隐私模型的基础上，拟议的解决方案将说明 供对手的外部知识和可自定义的敏感信息，以有效地达到平衡 在隐私和公用事业之间，与标准差异隐私模型相比，提高数据可用性。这 项目将推进目前的基因组数据匿名解决方案，并提高差异的可用性 隐私及其变体，目的是促进高度可用和保护隐私的数据共享。这项工作 将扩大获得基因组数据的访问，促进透明度并促进基因组的可重复性 申请。该项目符合国家人类基因组研究所（NHGRI）的使命 随着建议的技术增强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，并在生物医学中任命主要 信息计划，重点是基因组隐私技术，在美国一所主要的研究所大学。他的长期 术语目标是为数据共享和数据分析开发新的保护隐私技术，以便为了 促进基因组学和精确医学的合作研究工作。申请人提出仔细的 设计了职业发展计划，其中包括各种培训活动以完成计算机 具有额外生物医学知识的科学技能，并将其过渡到独立研究人员。 UCSD卫生部生物医学信息学将成为其职业生涯的杰出平台 开发，鉴于几个教师在隐私技术，计算生物学，基因组中的经验 医学，并与全球其他机构进行密切合作。