White Papers

Privacy-Preserving Data Mining on Data Grids in the Presence of Malicious Participants

Overview Data privacy is a major threat to the widespread deployment of data grids in domains such as health care and finance. The paper proposes a novel technique for obtaining knowledge - by way of a data mining model - from a data grid, while ensuring that the privacy is cryptographically secure. To the best of one's knowledge, all previous approaches for solving this problem fail in the presence of malicious participants. This paper presents an algorithm which, in addition to being secure against malicious members, is asynchronous, involves no global communication patterns, and dynamically adjusts to new data or newly added resources. As far as one knows, this is the first privacy-preserving data mining algorithm to possess these features in the presence of malicious participants.

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Publisher
Israel Institute of Technology
File Format
PDF
Date Published
Oct 1, 2008
Format
White Papers
Topics
Data Mining - Analysis, Security Management

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