A Survey of Privacy Preserving Utility Mining

2019 ◽  
pp. 207-232 ◽  
Author(s):  
Duy-Tai Dinh ◽  
Van-Nam Huynh ◽  
Bac Le ◽  
Philippe Fournier-Viger ◽  
Ut Huynh ◽  
...  
Keyword(s):  
Privacy Preserving ◽  
Utility Mining

scholarly journals Privacy Preserving Utility Mining: A Survey

2018 ◽  
Author(s):  
Wensheng Gan ◽  
Jerry Chun-Wei ◽  
Han-Chieh Chao ◽  
Shyue-Liang Wang ◽  
Philip S. Yu
Keyword(s):  
Privacy Preserving ◽  
Utility Mining

scholarly journals High utility-itemset mining and privacy-preserving utility mining

2016 ◽  
Vol 7 ◽  
pp. 74-80 ◽  
Author(s):  
Jerry Chun-Wei Lin ◽  
Wensheng Gan ◽  
Philippe Fournier-Viger ◽  
Lu Yang ◽  
Qiankun Liu ◽  
...  
Keyword(s):  
Privacy Preserving ◽  
Utility Mining ◽  
Itemset Mining ◽  
High Utility

scholarly journals An Improved Sanitization Algorithm in Privacy-Preserving Utility Mining

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xuan Liu ◽  
Genlang Chen ◽  
Shiting Wen ◽  
Guanghui Song
Keyword(s):  
Side Effects ◽  
Private Information ◽  
Pattern Mining ◽  
Privacy Preserving ◽  
Selection Strategy ◽  
Significant Information ◽  
Utility Mining ◽  
Maximum Utility ◽  
Minimum Number ◽  
Lower Ratio

High-utility pattern mining is an effective technique that extracts significant information from varied types of databases. However, the analysis of data with sensitive private information may cause privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, privacy-preserving utility mining (PPUM) has become an important research topic in recent years. The MSICF algorithm is a sanitization algorithm for PPUM. It selects the item based on the conflict count and identifies the victim transaction based on the concept of utility. Although MSICF is effective, the heuristic selection strategy can be improved to obtain a lower ratio of side effects. In our paper, we propose an improved sanitization approach named the Improved Maximum Sensitive Itemsets Conflict First Algorithm (IMSICF) to address this issue. It dynamically calculates conflict counts of sensitive items in the sanitization process. In addition, IMSICF chooses the transaction with the minimum number of nonsensitive itemsets and the maximum utility in a sensitive itemset for modification. Extensive experiments have been conducted on various datasets to evaluate the effectiveness of our proposed algorithm. The results show that IMSICF outperforms other state-of-the-art algorithms in terms of minimizing side effects on nonsensitive information. Moreover, the influence of correlation among itemsets on various sanitization algorithms’ performance is observed.

Privacy Preserving Data Utility Mining Architecture

2019 ◽  
pp. 253-268 ◽  
Author(s):  
Yousra Abdul Alsahib S. Aldeen ◽  
Mazleena Salleh
Keyword(s):  
Privacy Preserving ◽  
Utility Mining ◽  
Data Utility

Fast algorithms for hiding sensitive high-utility itemsets in privacy-preserving utility mining

2016 ◽  
Vol 55 ◽  
pp. 269-284 ◽  
Author(s):  
Jerry Chun-Wei Lin ◽  
Tsu-Yang Wu ◽  
Philippe Fournier-Viger ◽  
Guo Lin ◽  
Justin Zhan ◽  
...  
Keyword(s):  
Fast Algorithms ◽  
Privacy Preserving ◽  
Utility Mining ◽  
High Utility ◽  
High Utility Itemsets
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