Title :
Privacy preserving association rule mining with scalar product
Author :
Huang, Yiqun ; Lu, Zhengding ; Hu, And Heping
Author_Institution :
Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Hubei, China
fDate :
30 Oct.-1 Nov. 2005
Abstract :
One crucial aspect of distributed data mining is privacy preserving. Secure multiparty computation (SMC) is a useful approach to solve privacy preserving in distributed data mining. When data is vertically partitioned, scalar product is a feasible tool to securely discover frequent itemsets of association rule mining. We first show that several of the private scalar product protocols and analysis their insecurity. Then we develop a new and efficient protocol to perform two-party scalar product with an untrusted third party. The method is described in detail in this paper with complete analysis to demonstrate its effectiveness. Our protocol maintains integrity and high security of the data sets of each party while keeping communication and computation cost low.
Keywords :
cryptography; data integrity; data mining; data privacy; distributed algorithms; protocols; data set integrity; distributed algorithms; distributed data mining; frequent itemsets discovery; privacy preserving association rule mining; private scalar product protocols; secure multiparty computation; two-party scalar product; untrusted third party; Association rules; Computational efficiency; Data mining; Data privacy; Data security; Databases; Distributed computing; Itemsets; Protocols; Sliding mode control;
Conference_Titel :
Natural Language Processing and Knowledge Engineering, 2005. IEEE NLP-KE '05. Proceedings of 2005 IEEE International Conference on
Print_ISBN :
0-7803-9361-9
DOI :
10.1109/NLPKE.2005.1598836
