Privacy sensitive distributed data mining from multi-party data

Authors:
Hillol Kargupta;Kun Liu;Jessica Ryan
Affiliations:
Computer Science and Electrical Engineering Department, University of Maryland Baltimore County, Maryland;Computer Science and Electrical Engineering Department, University of Maryland Baltimore County, Maryland;Computer Science and Electrical Engineering Department, University of Maryland Baltimore County, Maryland
Venue:
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
Year:
2003

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Abstract

Privacy is becoming an increasingly important issue in datamining, particularly in security and counter-terrorism-related applicationswhere the data is often sensitive. This paper considers the problemof mining privacy sensitive distributed multi-party data. It specificallyconsiders the problem of computing statistical aggregates like the correlationmatrix from privacy sensitive data where the program for computingthe aggregates is not trusted by the owner(s) of the data. It presents abrief overview of a random projection-based technique to compute thecorrelation matrix from a single third-party data site and also multiplehomogeneous sites.