Privacy preserving association rule mining

The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker the requester and the provider at runtime. In particular new advances in data mining and knowledge discovers,, that allow for the extraction of hidden knowledge in enormous amount of data, impose new threats on the seamless integration of information. In this paper we consider the problem of building privacy preserving algorithm for one category of data mining techniques, the association ride mining. We introduce new metrics in order to demonstrate how security issues can be taken into consideration in the general framework of association ride mining, and we show that the complexity of the new heuristics is similar to this of the original algorithms.