Journal ArticleDOI
Secure, Privacy-Preserving Analysis of Distributed Databases.
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TLDR
This article shows how tools from information technology—specifically, secure multiparty computation and networking—can be used to perform statistically valid analyses of distributed databases, and presents protocols for securely performing regression, maximum likelihood estimation, and Bayesian analysis.Abstract:
In industrial and government settings, there is often a need to perform statistical analyses that require data stored in multiple distributed databases. However, the barriers to literally integrating these data can be substantial, even insurmountable. In this article we show how tools from information technology—specifically, secure multiparty computation and networking—can be used to perform statistically valid analyses of distributed databases. The common characteristic of these methods is that the owners share sufficient statistics computed on the local databases in a way that protects each owner's data from the other owners. Our focus is on horizontally partitioned data, in which data records rather than attributes are spread among the databases. We present protocols for securely performing regression, maximum likelihood estimation, and Bayesian analysis, as well as secure construction of contingency tables. We outline three current research directions: a software system implementing the protocols, se...read more
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Proceedings ArticleDOI
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records
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Proceedings ArticleDOI
Privacy-preserving matrix factorization
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Journal ArticleDOI
DataSHIELD: resolving a conflict in contemporary bioscience—performing a pooled analysis of individual-level data without sharing the data
Michael Wolfson,Susan E. Wallace,Nicholas G. D. Masca,Geoff Rowe,Nuala A. Sheehan,Vincent Ferretti,Philippe Laflamme,Martin D. Tobin,John Macleod,Julian Little,Isabel Fortier,Bartha Maria Knoppers,Paul Burton +12 more
TL;DR: The aim of this conceptual article is to encourage others to address the challenges and opportunities that DataSHIELD presents, and to explore potential extensions, for example to its use when different data sources hold different data on the same individuals.
References
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