Result Integrity Verification of Outsourced Privacy-preserving Frequent Itemset Mining

2015 ◽  
Author(s):  
Ruilin Liu ◽  
Hui (Wendy) Wang
Keyword(s):  
Privacy Preserving ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Integrity Verification ◽  
Itemset Mining

scholarly journals Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Chongjing Sun ◽  
Yan Fu ◽  
Junlin Zhou ◽  
Hui Gao
Keyword(s):  
Association Rule ◽  
Privacy Preserving ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Massive Data ◽  
Randomized Response ◽  
Rule Mining ◽  
Itemset Mining ◽  
Privacy Problem ◽  
Privacy Level

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy.

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