Secure, Privacy-Preserving Analysis of Distributed Databases

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, secure EM algorithms, and partially trusted third parties, which reduce incentives for owners to be dishonest.