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

项目摘要 保护隐私的分布式分析在广泛的生物医学研究中获得了越来越多的兴趣 社区，因为它可以a)消除创建、维护和安全访问中心的需要 数据存储库，b)最大限度地减少在数据拥有实体之外披露受保护的健康信息的需要， 以及c)减轻许多安全、专有、隐私和其他方面的担忧。因此，它在以下方面提供了巨大的承诺 降低跨多个机构协作的监管和其他障碍，并增强公众 信任生物医学研究。同样重要的是，对全美多个机构的健康数据进行分析 将产生更有力和更具普遍性的研究结果。这在癌症差异研究中尤其重要。 因为一家机构对少数群体的样本量可能很小。然而，仍然有很大的 当前保护隐私的分布式分析的最新技术中的方法论差距。最值得注意的是， 丢失的数据是一个巨大的挑战，因为它们在生物医学数据中无处不在，包括但不限于 电子健康记录(EHR)。众所周知，数据缺失是电子病历中偏差的一个主要来源。为 例如，来自少数群体的患者和那些较少获得私人保险的患者往往有 他们的电子病历中丢失了更多数据。众所周知，由于数据缺失而产生的有偏数据会产生不公平的统计和 机器学习模式，这反过来又会延续和加剧健康不平等和不平等。那里 一直没有关于在分布式分析中正确处理丢失数据的原则性方法的工作 我们最近的作品。此外，众所周知，分布式分析仍然存在揭示重要信息的风险 个人层面的信息和缺乏严格的保障意义上的差别隐私，盛行 隐私保护的概念和衡量标准。为了解决这些重要的限制，我们提出了三个具体的 目标。在目标1中，我们将改进和开发最先进的计算方法来处理丢失的数据 分布式分析并开发高级功能，通过差异增强隐私保护 隐私控制和同态加密。在目标1开发的方法的基础上，我们将开发一个 开放源码和开放访问的分布式分析平台，包括强大的系统架构和 AIM 2中用户友好的图形用户界面。我们将使用真实世界的使用来评估和验证我们的分布式分析平台 AIM中癌症差异的病例研究3.随着隐私保护的加强，我们的建议分发 分析平台将有可能进一步增强公众信任，降低协作门槛 横穿 多个 院校 在癌症研究方面。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较少的偏倚数据，增强了 研究结果，并提供 研究 在包括但不限于癌症差异在内的领域中的实质性好处 和信息学实践。

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.