Privacy preserving tree augmented Naïve bayesian multi-party implementation on horizontally partitioned databases

Authors:
Maria Eleni Skarkala;Manolis Maragoudakis;Stefanos Gritzalis;Lilian Mitrou
Affiliations:
Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece;Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece;Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece;Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece
Venue:
TrustBus'11 Proceedings of the 8th international conference on Trust, privacy and security in digital business
Year:
2011

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Abstract

The evolution of new technologies and the spread of the Internet have led to the exchange and elaboration of massive amounts of data. Simultaneously, intelligent systems that parse and analyze patterns within data are gaining popularity. Many of these data contain sensitive information, a fact that leads to serious concerns on how such data should be managed and used from data mining techniques. Extracting knowledge from statistical databases is an essential step towards deploying intelligent systems that assist in making decisions, but also must preserve the privacy of parties involved. In this paper, we present a novel privacy preserving data mining algorithm from statistical databases that are horizontally partitioned. The novelty lies to the multi-candidate election schema and its capabilities of being a basic foundation for a privacy preserving Tree Augmented Naïve Bayesian (TAN) classifier, in order to obviate disclosure of personal information.