SAFEGENOMES: Strong privacy Assurance For Effective GENOME Sharing

项目摘要 基因组数据对于推进医学研究和实现突破至关重要。然而，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 研究人员对数据的访问。为了促进数据驱动的基因组研究，解决隐私风险至关重要 在数据共享方面，并开发隐私保护解决方案来保护研究参与者。该项目将研究 现实攻击模型中的隐私风险，并开发平衡个人隐私和 共享数据的效用。总体而言，拟议的解决方案将使机构能够共享高可用性数据，同时 为数据贡献者提供强有力的隐私保障，促进数据收集和提高数据可用性。 在第一个目标中，将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已发布的数据。该框架将保护个人免受 识别并且还防止可以使用公共可用的表型(例如， 眼睛/头发颜色)。在第二个目标中，将针对现实的对手开发可定制的隐私解决方案 数据统计数据发布时的模型。基于最近的隐私模式，建议的解决方案将考虑到 对于对手的外部知识和可定制的敏感信息，有效地取得平衡 在隐私和效用之间，与标准的差异隐私模型相比，提高了数据的可用性。这 该项目将改进目前的基因组数据匿名化解决方案，并提高差异的可用性 隐私及其变体，目标是促进高度可用的和保护隐私的数据共享。这部作品 将扩大对基因组数据的获取，促进透明度，并促进基因组的重复性 申请。该项目符合国家人类基因组研究所(NHGRI)的使命， 由于所提出的技术加强了数据共享，并促进了协作基因组研究。 申请者的职业目标是成为一名独立的调查员，主要任职于生物医学 信息学项目，重点是基因组隐私技术，在美国一所主要的研究型大学。他长长的- 学期目标是为数据共享和数据分析开发新的隐私保护技术，以便 促进基因组学和精准医学领域的合作研究工作。申请人提出了一份仔细的 设计了职业发展计划，其中包括各种培训活动，以补充他的计算机 拥有更多生物医学知识的科学技能，并顺利过渡到独立研究人员。 加州大学圣迭戈分校生物医学信息学健康系将为他的职业生涯提供一个特殊的平台 发展，考虑到几个教师在隐私技术、计算生物学、基因组学方面的经验 医学，以及与世界各地其他机构的密切合作。

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.