We present techniques for privacy-preserving computation of multidimensional aggregates on data partitioned across multiple clients. Data from different clients is perturbed (rand...
Rakesh Agrawal, Ramakrishnan Srikant, Dilys Thomas
There is growing public concern about personal data collected by both private and public sectors. People have very little control over what kinds of data are stored and how such da...
Data Management portfolio within an organization has seen an upsurge in initiatives for compliance, security, repurposing and storage within and outside the organization. When suc...
Privacy is an increasingly important aspect of data publishing. Reasoning about privacy, however, is fraught with pitfalls. One of the most significant is the auxiliary informatio...
Abstract. In this paper, we present a protocol for computing the principal eigenvector of a collection of data matrices belonging to multiple semi-honest parties with privacy const...