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Fast privacy-preserving utility mining algorithm based on utility-list dictionary
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AI
DOI：10.1007/s10489-023-04791-2.png)
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Abstract
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Privacy preserving utility mining (PPUM) aims to solve the problem of sensitive information leakage in utility pattern mining. In recent years, researchers have proposed algorithms to solve the privacy-preserving problem. However, these algorithms have high side effects, long sanitization time, and computational complexity. Although the FPUTT algorithm reduces the number of database scans, tree construction and traversal still take much time. The paper proposes a fast utility-list dictionary algorithm (FULD). The utility-list dictionary consists of all sensitive items. Through dictionary lookup, sensitive items can be found and modified. In addition, the novel concepts of SINS and tns are proposed to reduce the side effects of the algorithm. In this paper, the experiments show that the FULD algorithm has good performance, such as running time and side effects. The running time of the FULD is 15-20 times shorter than the FPUTT algorithm. It performs well both on sparse and dense datasets.
Keywords:
Sensitive information
Utility mining
Privacy preserving
Utility-list dictionary
Utility lists
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