Result integrity verification of outsourced privacy-preserving frequent itemset mining

In the recently-emerged Data-Mining-as-a-Service (DMaS) paradigm, a client outsources her data and the data mining needs to a third party service provider. It raises a few security issues including privacy protection and result integrity verification. Most of the recent work studied these two issues separately. In this paper, we focus on the problem of result integrity verification of outsourced privacy-preserving frequent itemset mining. It is challenging to discover the incorrect results by the service provider's misbehaviors from the mining output that intends to be inaccurate due to privacy protection techniques. We design efficient approaches that can provide high probabilistic guarantee for both correctness and completeness of the frequent itemset mining results. Our experiment results show the efficiency and effectiveness of our approaches.