CAREER: Privacy-preserving learning for distributed data

成像和测序等医疗技术使得以越来越低的成本收集大量信息成为可能。共享研究数据可以促进科学理解并改善医疗保健结果。然而，对患者隐私的关注可能会妨碍开放数据共享，从而阻碍在了解精神健康障碍等污名化疾病方面取得进展。 本研究旨在了解如何以量化和严格保护数据隐私的方式分析和学习不同站点（如医疗中心）保存的敏感数据。 在这项研究中使用的框架是差分隐私，最近提出的模型，用于测量数据共享中的隐私风险。 差分隐私算法提供近似（噪声）答案来保护敏感数据，涉及隐私和实用性之间的权衡。 本研究探讨如何将不同地点的私有近似值联合收割机，以改善结果的整体品质或效用。这项研究的主要目标是了解私人数据共享的基本限制，设计算法，使私人近似和规则组合它们，并了解具有更复杂的隐私和共享限制的网站的后果。 用于解决这些问题的方法是从统计，计算机科学和电气工程的数学技术的混合。这项研究的教育部分将涉及设计介绍性的大学课程和材料的数据科学，本科研究项目，研究生课程的课程材料，并通过演示文稿，教程材料和开源软件扩展到不断增长的数据黑客社区。本研究的主要目的是通过开发实用的隐私保护算法的算法原理来弥合理论与实践之间的差距。这些算法将在用于理解和诊断心理健康障碍的神经成像数据上进行验证。实施这项研究的结果将为医疗保健和其他领域的研究建立实用的隐私保护学习创造蓝图。 分布式系统中隐私和效用之间的权衡自然会导致学习问题的成本效益权衡的更一般问题，并且相同的算法原理将揭示一般分布式系统中的信息处理和机器学习，其中消息可能是嘈杂的或损坏的。

Medical technologies such as imaging and sequencing make it possible to gather massive amounts of information at increasingly lower cost. Sharing data from studies can advance scientific understanding and improve healthcare outcomes. Concern about patient privacy, however, can preclude open data sharing, thus hampering progress in understanding stigmatized conditions such as mental health disorders. This research seeks to understand how to analyze and learn from sensitive data held at different sites (such as medical centers) in a way that quantifiably and rigorously protects the privacy of the data. The framework used in this research is differential privacy, a recently-proposed model for measuring privacy risk in data sharing. Differentially private algorithms provide approximate (noisy) answers to protect sensitive data, involving a tradeoff between privacy and utility. This research studies how to combine private approximations from different sites to improve the overall quality or utility of the result. The main goals of this research are to understand the fundamental limits of private data sharing, to design algorithms for making private approximations and rules for combining them, and to understand the consequences of sites having more complex privacy and sharing restrictions. The methods used to address these problems are a mix of mathematical techniques from statistics, computer science, and electrical engineering.The educational component of this research will involve designing introductory university courses and material on data science, undergraduate research projects, curricular materials for graduate courses, and outreach to the growing data-hacker community via presentations, tutorial materials, and open-source software. The primary aim of this research is bridge the gap between theory and practice by developing algorithmic principles for practical privacy-preserving algorithms. These algorithms will be validated on neuroimaging data used to understand and diagnose mental health disorders. Implementing the results of this research will create a blueprint for building practical privacy-preserving learning for research in healthcare and other fields. The tradeoffs between privacy and utility in distributed systems lead naturally to more general questions of cost-benefit tradeoffs for learning problems, and the same algorithmic principles will shed light on information processing and machine learning in general distributed systems where messages may be noisy or corrupted.