Robust privacy preserving distributed analysis platform for cancer research: addressing data bias and disparities

用于癌症研究的强大隐私保护分布式分析平台：解决数据偏差和差异

• 批准号: 10642562
• 负责人: Xiaoqian Jiang
• 金额: $ 41.19万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10642562
• 关键词: Address; Architecture; Area; Biomedical Research; Client; Collaborations; Communities; Data; Data Correlations; Disparity; Electronic Health Record; Ensure; FAIR principles; Feedback; Geographic Locations; Health Policy; Health protection; Healthcare; Individual; Informatics; Institution; Insurance; Lead; Legal; Malignant Neoplasms; Medicine; Methodology; Methods; Minority Groups; Modeling; Patients; Pattern; Privacy; Privatization; Process; Research; Research Personnel; Risk; Sample Size; Sampling Biases; Secure; Security; Services; Source; Systems Analysis; Training; Work; anticancer research; cancer care; cancer health disparity; care delivery; data privacy; data repository; encryption; health data; health disparity; health inequalities; improved; innovation; interest; machine learning model; marginalization; minority patient; novel; open source; privacy preservation; privacy protection; public trust; research study; statistical and machine learning; system architecture; tool; underserved minority; usability; user-friendly

Project Summary Privacy-preserving distributed analysis has gained increasing interests in the broad biomedical research community in recent years, as it can a) eliminate the need to create, maintain, and secure access to central data repositories, b) minimize the need to disclose protected health information outside the data-owning entity, and c) mitigate many security, proprietary, privacy and other concerns. As such, it offers great promises in lowering regulatory and other hurdles for collaboration across multiple institutions and enhancing the public trust in biomedical research. Equally important, analysis of health data from multiple institutions across the US would yield more robust and generalizable findings. This is particularly relevant in cancer disparities research as the sample size for minority groups can be very small from one institution. However, there remain significant methodological gaps in the current state-of-the-art for privacy-preserving distributed analysis. Most notably, missing data present significant challenges, as they are ubiquitous in biomedical data including, but not limited to, electronic health records (EHR). It is well known that missing data is a major source of bias in EHR. For example, patients from minority groups and those who have less access to private insurance tend to have more missing data in their EHR. Biased data as a result of missing data are known to yield unfair statistical and machine learning models, which in turn can perpetuate and exacerbate health inequities and disparities. There has been no work on principled approaches for properly handling missing data in distributed analysis beyond our recent works. In addition, it is well-known that distributed analysis is still at risk of revealing important individual-level information and lacks rigorous guarantee in the sense of differential privacy, the prevailing notion and metric for privacy protection. To address these significant limitations, we propose three specific aims. In Aim 1, we will refine and develop state-of-the-art imputation methods for handling missing data in distributed analysis and develop advanced functionalities for enhanced privacy protection through differential privacy control and homomorphic encryption. Building on the methods developed in Aim 1, we will develop an open-source and open-access distributed analysis platform that includes a robust system architecture and user-friendly GUI in Aim 2. We will assess and validate our distributed analysis platform using real-world use cases in cancer disparities research in Aim 3. With the enhanced privacy protection, our proposed distributed analysis platform will have the potential to further enhance public trust and lowerhurdles for collaboration across multiple institutions in cancer research. As such, our platform will enable researchers to use more information and less biased data in cancer research, enhance the validity, robustness and generalizability of research findings, and offer research substantial benefits in areas including, but not limited to, cancer disparities and informatics practice.

项目摘要 隐私性分布式分析已在广泛的生物医学研究中获得了越来越多的利益 近年来社区，因为它可以a）消除创建，维护和确保访问中央的需求 数据存储库，b）最大程度地减少需要在数据拥有实体之外披露受保护的健康信息的需求， c）减轻许多安全，专有，隐私和其他问题。因此，它提供了巨大的承诺 降低监管和其他障碍，以跨多个机构合作并增强公众 信任生物医学研究。同样重要的是，分析来自美国多个机构的健康数据 将产生更强大且可推广的发现。这在癌症差异研究中尤其重要 由于少数群体的样本量可能很小。但是，仍然存在重大 用于保护隐私分布分析的当前最新方法中的方法论差距。最值得注意的是 缺少数据提出了重大挑战，因为它们在生物医学数据中无处不在，包括但不限 到，电子健康记录（EHR）。众所周知，缺少数据是EHR中偏差的主要来源。为了 例如，来自少数群体和少数获得私人保险的患者倾向于 他们的EHR中有更多丢失的数据。由于缺少数据而导致的偏差数据已知会产生不公平的统计和 机器学习模型又可以使健康不平等和差异延续。那里 在有原则的方法上没有工作以适当处理分布式分析中的丢失数据以外 我们最近的作品。此外，众所周知，分布式分析仍然有揭示重要的风险 个人级别的信息，并且缺乏差异隐私意义上的严格保证 隐私保护的概念和指标。为了解决这些重大限制，我们提出了三个特定的特定限制 目标。在AIM 1中，我们将完善并开发最先进的插定方法来处理丢失的数据 分布式分析并发展高级功能，以通过差异增强隐私保护 隐私控制和同态加密。在AIM 1中开发的方法的基础上，我们将开发一个 开源和开放访问分布式分析平台，其中包括强大的系统体系结构和 AIM 2中的用户友好GUI。我们将使用现实世界使用评估和验证分布式分析平台 AIM 3中的癌症差异研究病例。随着隐私保护的增强，我们提议的分布 分析平台将有可能进一步增强公众信任和下赫尔德群岛的协作 穿过 多种的 机构 在癌症研究中。因此，我们的平台将使研究人员能够使用更多 癌症研究中的信息和偏见的数据，提高了有效性，鲁棒性和普遍性 研究发现，并提供 研究 在包括但不限于癌症差异在内的领域的重大好处 和信息学实践。