Privacy preserving association rules mining on distributed homogenous databases

Privacy is one of the most important properties that an information system must satisfy. In these systems, there is a need to share information among different, not trusted entities, and the protection of sensible information has a relevant role. A relatively new trend shows that classical access control techniques are not sufficient to guarantee privacy preserving when data mining techniques are used in a malicious way. Privacy preserving data mining algorithms have been recently introduced with the aim of preventing the discovery of sensible information. In this paper, we propose a modification to privacy preserving association rule mining algorithm on distributed homogenous database. Our algorithm is faster, privacy preserving and provides accurate results. The flexibility for extension to any number of sites can be achieved without any change in the implementation. Also any increase in number of these sites does not add more time overhead, because all client sites perform the mining process in the same time so the overhead is in communication time only. Finally, the total bit-communication cost for our algorithm is function in (N) sites.