Privacy Preserving Sequential Pattern Mining Based on Secure Multi-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: multiple parties, each having a private data set, wish to collaboratively discover sequential patterns on the union of the their private data sets respectively without disclosing their private data to any other party. Therefore, we put forward a novel approach to discover privacy-preserving sequential patterns based on secure multi-party computation using homomorphic encryption technology