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。共享来自研究的数据可以促进科学理解并改善医疗保健结果。然而，对患者隐私的担忧可能会阻碍公开数据共享，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可量化和严格保护数据隐私的方式分析和学习不同地点（如医疗中心）持有的敏感数据。本研究中使用的框架是差分隐私，这是最近提出的一种用于测量数据共享中隐私风险的模型。差分私有算法提供近似的（有噪声的）答案来保护敏感数据，涉及隐私和实用之间的权衡。本研究研究如何结合来自不同地点的私人近似来提高结果的整体质量或效用。本研究的主要目标是了解私人数据共享的基本限制，设计用于进行私人近似的算法和组合它们的规则，并了解具有更复杂隐私和共享限制的网站的后果。用于解决这些问题的方法混合了统计学、计算机科学和电气工程的数学技术。这项研究的教育部分将包括设计大学数据科学入门课程和材料、本科生研究项目、研究生课程的课程材料，以及通过演示、教程材料和开源软件向不断增长的数据黑客社区推广。本研究的主要目的是通过开发实用隐私保护算法的算法原则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精神健康障碍的神经成像数据上得到验证。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-收益权衡的更普遍问题，并且相同的算法原则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.