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http://dx.doi.org/10.13089/JKIISC.2013.23.3.383

Dummy Data Insert Scheme for Privacy Preserving Frequent Itemset Mining in Data Stream  

Jung, Jay Yeol (Graduate School of Information Management and Security, Korea University)
Kim, Kee Sung (Graduate School of Information Management and Security, Korea University)
Jeong, Ik Rae (Graduate School of Information Management and Security, Korea University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.23, no.3, 2013 , pp. 383-393 More about this Journal
Abstract
Data stream mining is a technique to obtain the useful information by analyzing the data generated in real time. In data stream mining technology, frequent itemset mining is a method to find the frequent itemset while data is transmitting, and these itemsets are used for the purpose of pattern analyze and marketing in various fields. Existing techniques of finding frequent itemset mining are having problems when a malicious attacker sniffing the data, it reveals data provider's real-time information. These problems can be solved by using a method of inserting dummy data. By using this method, a attacker cannot distinguish the original data from the transmitting data. In this paper, we propose a method for privacy preserving frequent itemset mining by using the technique of inserting dummy data. In addition, the proposed method is effective in terms of calculation because it does not require encryption technology or other mathematical operations.
Keywords
Data Stream; Privacy Preserving; Frequent Itemset mining;
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