Privacy sensitive distributed data mining from multi-party data

Abstract

Privacy is becoming an increasingly important issue in data mining, particularly in security and counter-terrorism-related applications where the data is often sensitive. This paper considers the problem of mining privacy sensitive distributed multi-party data. It specifically considers the problem of computing statistical aggregates like the correlation matrix from privacy sensitive data where the program for computing the aggregates is not trusted by the owner(s) of the data. It presents a brief overview of a random projection-based technique to compute the correlation matrix from a single third-party data site and also multiple homogeneous sites. © Springer-Verlag Berlin Heidelberg 2003.