Tools to Address the Challenges of Preserving Privacy in Sharing and Analysis of Biomedical Data

应对生物医学数据共享和分析中保护隐私挑战的工具

基本信息

批准号：
10708820
负责人：
Gamze Gursoy
金额：
$ 42.85万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
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：
2022-09-23 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10708820
关键词：
Address Adopted Algorithms Bioinformatics Biological Clinical Clinical Data Computer software Conflict (Psychology)Data Data Security Democracy Development Disease Epigenetic Process Extravasation Future Genetic Genomics Genotype Goals Human Individual Medical Methods Mining Modality Molecular Participant Patients Phenotype Population Privacy Privatization Research Research Personnel Trust data access data privacy human data novel patient privacy phenotypic data prevent privacy preservation privacy protection theories tool transcriptomics

Address Adopted Algorithms Bioinformatics Biological Clinical Clinical Data Computer software Conflict (Psychology)Data Data Security Democracy Development Disease Epigenetic Process Extravasation Future Genetic Genomics Genotype Goals Human Individual Medical Methods Mining Modality Molecular Participant Patients Phenotype Population Privacy Privatization Research Research Personnel Trust data access data privacy human data novel patient privacy phenotypic data prevent privacy preservation privacy protection theories tool transcriptomics

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项目摘要

Project Summary Understanding a single disease or condition requires large-scale mining of genetic, epigenetic, environmental, and clinical observations from large amounts of human data. Widespread and easy access to such data is imperative to decipher the vast trove of data that has been collected at the individual and population level. However, there is a direct conflict between protecting the privacy of patients and research participants and sharing genetic, epigenetic, and clinical data for biomedical advances. This conflict is partly due to a disconnect between the fields that generate and analyze personal biological data and the fields that establish theories and implementations for data privacy and security. The goal of this project is two-fold: 1) to quantify private information leakages from various types of human-derived biological data in a systematic manner to inform data-sharing efforts and 2) to develop privacy-enhancing analysis software for data at various molecular levels (genomics, transcriptomics, phenotype data) at scale. We will create an evolving and modular tool suite to both quantify and preserve privacy; this suite will have the ability to be adopted to new data modalities and analysis needs as they arise. The proposed tools will help prevent future catastrophic privacy leaks, which may result in a loss of access to all medically actionable data; democratize data access for all researchers; and create trust between patients and researchers, thus increasing participation in studies.

项目摘要了解单一疾病或状况需要大规模挖掘遗传，表观遗传学，来自大量人类数据的环境和临床观察。广泛和轻松访问此类数据至关重要的是破译已收集的大量数据在个人和人口层面。但是，保护患者和研究参与者的隐私以及共享遗传，表观遗传和临床数据生物医学的进步。这种冲突部分是由于生成的字段之间的断开连接并分析个人生物学数据以及建立理论和实施的领域用于数据隐私和安全性。该项目的目的是两个方面：1）量化私人信息以系统的方式从各种类型的人类衍生的生物学数据中泄漏，以告知数据共享工作和2）为各种数据开发数据的隐私增强分析软件分子水平（基因组学，转录组学，表型数据）的规模。我们将创建一个不断发展的和模块化工具套件以量化和保留隐私；这个套房将有能力按照新的数据方式和分析需求采用。提出的工具将有助于防止将来的灾难性隐私泄漏，这可能导致所有医学上的访问权限丧失可操作的数据；将所有研究人员的数据访问民主化；并在患者之间建立信任和研究人员，从而增加了参与研究。