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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：
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
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/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

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.

项目总结