Efficient Apriori based algorithms for privacy preserving frequent itemset mining

Author

Csiszarik, Adrian ; Lestyan, Szilvia ; Lukacs, Andras

Author_Institution

Inst. of Math., Inter-Univ. Centre for Telecommun. & Inf., Hungary

fYear

2014

fDate

5-7 Nov. 2014

Firstpage

431

Lastpage

435

Abstract

Frequent Itemset Mining as one of the principal routine of data analysis and a basic tool of large scale information aggregation also bears a serous interest in Privacy Preserving Data Mining. In this paper Apriori based distributed, privacy preserving Frequent Itemset Mining algorithms are considered. Our secure algorithms are designed to fit in the Secure Multiparty Computation model of privacy preserving computation.

Keywords

data analysis; data mining; data privacy; security of data; Apriori based algorithms; Apriori based distributed privacy preserving frequent itemset mining algorithms; data analysis; large scale information aggregation; privacy preserving data mining; secure algorithms; secure multiparty computation model; Algorithm design and analysis; Data privacy; Itemsets; Partitioning algorithms; Privacy;

fLanguage

English

Publisher

ieee

Conference_Titel

Cognitive Infocommunications (CogInfoCom), 2014 5th IEEE Conference on

Conference_Location

Vietri sul Mare

Type

conf

DOI

10.1109/CogInfoCom.2014.7020493

Filename

7020493

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=240191