Privacy Preserving Sequential Pattern Mining Based on Secure Two-Party Computation

Privacy-preserving data mining in distributed or grid environment is an important hot research topic in recent years. We focus on the privacy-preserving sequential pattern mining in the following situation: two parties, each having a private data set, wish to collaboratively discover sequential patterns on the union of the two private data sets without disclosing their private data to each other. Therefore, we put forward a novel approach to discover privacy-preserving sequential patterns based on secure two-party computation using homomorphic encryption technology