CAREER: Privacy-preserving learning for distributed data

职业：分布式数据的隐私保护学习

• 批准号: 1453432
• 负责人: Anand Sarwate
• 金额: $ 54万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453432&HistoricalAwards=false
• 关键词: CAREER; Privacy; preserving; learning; distributed

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.

成像和测序等医疗技术使以越来越低的成本收集大量信息成为可能。共享研究数据可以提高科学理解并改善医疗保健结果。但是，对患者隐私的担忧可能会排除开放数据共享，从而阻碍了理解诸如心理健康障碍之类的污名化状况的进展。 这项研究试图了解如何以量化和严格保护数据隐私的方式分析和从不同站点（例如医疗中心）的敏感数据中学习。 这项研究中使用的框架是差异隐私，这是一个最近验证的模型，用于衡量数据共享中的隐私风险。 差异化私有算法提供了近似（嘈杂）的答案，以保护敏感数据，涉及隐私和公用事业之间的权衡。 该研究研究了如何结合不同站点的私人近似值，以提高结果的总体质量或效用。这项研究的主要目标是了解私人数据共享的基本限制，设计用于制定私人近似值和合并规则的算法，并了解具有更复杂隐私和共享限制的站点的后果。 用于解决这些问题的方法是统计学，计算机科学和电气工程的数学技术的混合。本研究的教育部分将涉及设计入门大学课程和数据科学材料，本科研究项目，研究生课程的课程材料以及向成长的数据建造者社区通过成长的数据 - 培养基介绍和开放式材料和开放式材料和开放式材料和开放式材料和开放式材料和开放式软件。这项研究的主要目的是通过为实用隐私算法开发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影像学数据上进行验证。实施这项研究的结果将为建立医疗保健和其他领域研究的实用隐私学习学习创建蓝图。 在分布式系统中的隐私与公用事业之间的权衡自然而然地导致了有关学习问题的成本效益折衷问题，而相同的算法原则将揭示信息处理和机器学习在一般分布式系统中可能嘈杂或腐败的一般分布式系统。

项目成果

Analysis of a privacy-preserving PCA algorithm using random matrix theory

基于随机矩阵理论的隐私保护PCA算法分析

• DOI: 10.1109/globalsip.2016.7906058
• 发表时间: 2016
• 期刊: 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP
• 作者: Wei, Lu;Sarwate, Anand D.;Corander, Jukka;Hero, Alfred;Tarokh, Vahid
• 通讯作者: Tarokh, Vahid

Optimal differential privacy mechanisms under Hamming distortion for structured source classes

• DOI: 10.1109/isit.2016.7541663
• 发表时间: 2016-07
• 期刊: 2016 IEEE International Symposium on Information Theory (ISIT)
• 作者: Kousha Kalantari;L. Sankar;A. Sarwate

Data-weighted ensemble learning for privacy-preserving distributed learning

• DOI: 10.1109/icassp.2016.7472089
• 发表时间: 2016-03
• 期刊: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Liyang Xie;S. Plis;A. Sarwate

Large scale collaboration with autonomy: Decentralized data ICA

• DOI: 10.1109/mlsp.2015.7324344
• 发表时间: 2015-11
• 期刊: 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)
• 作者: Bradley T. Baker;Rogers F. Silva;V. Calhoun;A. Sarwate;S. Plis

Distributed Differentially Private Algorithms for Matrix and Tensor Factorization

• DOI: 10.1109/jstsp.2018.2877842
• 发表时间: 2018-12-01
• 期刊: IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
• 影响因子: 7.5
• 作者: Imtiaz, Hafiz;Sarwate, Anand D.
• 通讯作者: Sarwate, Anand D.