CAREER: Privacy-preserving learning for distributed data

成像和测序等医学技术使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，改善医疗保健结果。然而，对患者隐私的担忧可能会阻碍开放数据共享，从而阻碍在理解诸如精神健康障碍等污名化疾病方面的进展。这项研究试图了解如何分析和学习不同站点(如医疗中心)持有的敏感数据，并以一种量化和严格保护数据隐私的方式进行学习。本研究使用的框架是差异隐私，这是最近提出的一种衡量数据共享中隐私风险的模型。不同的私有算法提供近似(噪声)答案来保护敏感数据，涉及隐私和效用之间的权衡。本研究研究如何结合来自不同网站的私人近似，以提高结果的整体质量或效用。这项研究的主要目标是了解隐私数据共享的基本限制，设计进行隐私近似的算法和组合它们的规则，并了解网站具有更复杂的隐私和共享限制的后果。用来解决这些问题的方法是统计学、计算机科学和电子工程的数学技术的组合。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、本科研究项目、研究生课程材料，以及通过演示、教程材料和开源软件向日益增长的数据黑客社区拓展。这项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和实践之间的差距。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创建一个实用的隐私保护学习的蓝图。分布式系统中隐私和效用之间的权衡自然导致了学习问题的成本-收益权衡的更一般问题，同样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，在这些系统中，消息可能是噪声或损坏的。

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.