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A new algorithm using integer programming relaxation for privacy-preserving in utility mining
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AI
DOI：10.1007/s10489-023-04913-w.png)
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Abstract
En 中文
High-utility itemset mining (HUIM) is an effective technique for discovering significant information in data. However, data containing sensitive and private information may cause privacy concerns. Therefore, privacy preserving utility mining (PPUM) has recently become a critical research area. PPUM is the process of transforming a quantitative transactional database into a sanitised one, thus ensuring that utility mining algorithms cannot discover sensitive information. The sanitisation process can have several side effects, including the loss of non-sensitive information and the introduction of redundant information. Additionally, the running times of heuristic algorithms for sanitising data are high. To minimise negative effects and lower the execution time of the hiding process, we propose the G-ILP algorithm with a GPU parallel programming method for preprocessing and a new efficient constraint satisfaction problem for hiding data. The experimental evaluations of G-ILP show the algorithm's efficiency in terms of running time and its ability to minimise side effects in large datasets.
Keywords:
Privacy preserving
Data mining
Utility mining
Sanitization
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