A framework for privacy preserving statistical analysis on distributed databases

Abstract

Alice and Bob are mutually untrusting curators who possess separate databases containing information about a set of respondents. This data is to be sanitized and published to enable accurate statistical analysis, while retaining the privacy of the individual respondents in the databases. Further, an adversary who looks at the published data must not even be able to compute statistical measures on it. Only an authorized researcher should be able to compute marginal and joint statistics. This work is an attempt toward providing a theoretical formulation of privacy and utility for problems of this type. Privacy of the individual respondents is formulated using ϵ-differential privacy. Privacy of the marginal and joint statistics on the distributed databases is formulated using a new model called δ-distributional ϵ-differential privacy. Finally, a constructive scheme based on randomized response is presented as an example mechanism that satisfies the formulated privacy requirements.