DocumentCode :
2716326
Title :
Secure two and multi-party association rule mining
Author :
Samet, Saeed ; Miri, Ali
Author_Institution :
Sch. of Inf. Technol. & Eng. (SITE), Univ. of Ottawa, Ottawa, ON, Canada
fYear :
2009
fDate :
8-10 July 2009
Firstpage :
1
Lastpage :
6
Abstract :
Association rule mining provides useful knowledge from raw data in different applications such as health, insurance, marketing and business systems. However, many real world applications are distributed among two or more parties, each of which wants to keep its sensitive information private, while they collaboratively gaining some knowledge from their data. Therefore, secure and distributed solutions are needed that do not have a central or third party accessing the parties´ original data. In this paper, we present a new protocol for privacy-preserving association rule mining to overcome the security flaws in existing solutions, with better performance, when data is vertically partitioned among two or more parties. Two sub-protocols for secure binary dot product and cardinality of set intersection for binary vectors are also designed which are used in the main protocols as building blocks.
Keywords :
data mining; data privacy; vectors; binary vector; multiparty association rule mining; privacy-preserving association rule mining; security protection; Association rules; Bayesian methods; Classification tree analysis; Computational intelligence; Data mining; Data privacy; Data security; Information security; Insurance; Protocols; Association rules; Data mining; Distributed data structures; Mining methods and algorithms; Security and privacy protection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Security and Defense Applications, 2009. CISDA 2009. IEEE Symposium on
Conference_Location :
Ottawa, ON
Print_ISBN :
978-1-4244-3763-4
Electronic_ISBN :
978-1-4244-3764-1
Type :
conf
DOI :
10.1109/CISDA.2009.5356544
Filename :
5356544
Link To Document :
بازگشت