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

Author

Ouyang, Wei-min ; Huang, Qin-hua

Author_Institution

Manage. Dept., Shanghai Univ. of Sport

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

1227

Lastpage

1232

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: 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

Keywords

cryptography; data mining; data privacy; database management systems; grid environment; homomorphic encryption technology; privacy-preserving data mining; privacy-preserving sequential pattern mining; private data set; secure two-party computation; Computer networks; Cryptographic protocols; Cryptography; Cybernetics; Data mining; Data privacy; Data security; Databases; Distributed computing; Environmental management; Grid computing; Itemsets; Machine learning; Sliding mode control; Transaction databases; Privacy Preserving; Secure Two-party Computation; Sequential Pattern Mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258643

Filename

4028251