Privacy Preserving Sequential Pattern Mining Based on Secure Multi-party Computation

Author

Ouyang, Weimin ; Huang, Qinhua

Author_Institution

Manage. Dept., Shanghai Univ. of Sport

fYear

2006

fDate

20-23 Aug. 2006

Firstpage

149

Lastpage

154

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Acquisition, 2006 IEEE International Conference on

Conference_Location

Weihai

Print_ISBN

1-4244-0528-9

Electronic_ISBN

1-4244-0529-7

Type

conf

DOI

10.1109/ICIA.2006.305984

Filename

4097917