Title :
Privacy Preserving Sequential Pattern Mining Based on Secure Multi-party Computation
Author :
Ouyang, Weimin ; Huang, Qinhua
Author_Institution :
Manage. Dept., Shanghai Univ. of Sport
Abstract :
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
Keywords :
cryptography; data mining; data privacy; grid computing; pattern classification; data mining; distributed environment; grid environment; homomorphic encryption technology; multiparty computation security; privacy preserving sequential pattern mining; Computer networks; Conference management; Cryptographic protocols; Cryptography; Data mining; Data privacy; Databases; Distributed computing; Engineering management; Sliding mode control;
Conference_Titel :
Information Acquisition, 2006 IEEE International Conference on
Conference_Location :
Weihai
Print_ISBN :
1-4244-0528-9
Electronic_ISBN :
1-4244-0529-7
DOI :
10.1109/ICIA.2006.305984
