Affiliations: [a] Department of Computer Science, Electronic Engineering and Mathematical Science Western Norway University of Applied Sciences, Bergen, Norway
| [b] Department of Mathematics & Computer Science, Brandon University, Brandon, Canada
| [c] Research Centre for Interneural Computing, China Medical University, Taiwan
| [d] Department of Computer Science & Information Engineering, Asia University, Taiwan
| [e] Department of Medical Research, China Medical University Hospital, China Medical University Taichung, Taiwan
Correspondence:
[*]
Corresponding author. Jerry Chun-Wei Lin, Department of Computer Science, Electronic Engineering and Mathematical Science, Western Norway University of Applied Sciences, Bergen, Norway. E-mail: jerrylin@ieee.org.
Abstract: Frequent pattern mining (FIM) identifies the most important patterns in data sets. However, due to the huge and high-dimensional nature of transactional data, classical pattern mining techniques suffer from the limitations of dimensions and data annotations. Recently, data mining while preserving privacy is considered as an important research area. Information privacy is a tradeoff that must be considered when using data. Through many years, privacy-preserving data mining (PPDM) made use of methods that are mostly based on heuristics. The operation of deletion was used to hide the sensitive information in PPDM. In this study, we used deep active learning to protect private and sensitive information. This paper combines entropy-based active learning with an attention-based approach to effectively hide sensitive patterns. The constructed models are then validated using high-dimensional transactional data with attention-based and active learning methods in a reinforcement environment. The results show that the proposed model can support and improve the effectiveness of decision-making by increasing the number of training instances through the use of a pooling technique and an entropy uncertainty measure. The proposed paradigm can achieve data sanitization by the hiding sensitive items and avoiding to hide the non-sensitive items. The model outperforms greedy, genetic, and particle swarm optimization approaches.
Keywords: deep learning, attention network, data mining, reinforcement learning, classification
