The density-based clustering method for privacy-preserving data mining

Jimmy Ming-Tai Wu; Jerry Chun-Wei Lin; Philippe Fournier-Viger; Youcef Djenouri; Chun-Hao Chen; Zhongcui Li; Jimmy Ming-Tai Wu; Jerry Chun-Wei Lin; Philippe Fournier-Viger; Youcef Djenouri; Chun-Hao Chen; Zhongcui Li

doi:10.3934/mbe.2019082

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 3: 1718-1728. doi: 10.3934/mbe.2019082

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The density-based clustering method for privacy-preserving data mining

1.
College of Computer Science and Engineering, Shandong University of Science and Technology, Qindao, Shandong, China
2.
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
3.
Department of Computing, Mathematics, and Physcis, Western Norway University of Applied Sciences, Bergen, Norway
4.
School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, China
5.
Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
6.
Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan

Received: 14 December 2018 Accepted: 31 January 2019 Published: 27 February 2019

Privacy-preserving data mining has become an interesting and emerging issue in recent years since it can, not only hide the sensitive information but still mine the meaningful knowledge at the same time. Since privacy-preserving data mining is a non-trivial task, which is also concerned as a NP-hard problem, several evolutionary algorithms were presented to find the optimized solutions but most of them focus on considering a single-objective function with the pre-defined weight values of three side effects (hiding failure, missing cost, and artificial cost). In this paper, we aim at designing a multiple objective particle swarm optimization method for hiding the sensitive information based on the density clustering approach (named CMPSO). The presented CMPSO is more flexible to select the most appropriate solutions for hiding the sensitive information based on user's preference. Extensive experiments are carried on two datasets to show that the designed CMPSO algorithm has good performance than the traditional single-objective evolutionary approaches in terms of three side effects.
- density clustering,
- Pareto solutions,
- optimization,
- PPDM,
- deletion
Citation: Jimmy Ming-Tai Wu, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Youcef Djenouri, Chun-Hao Chen, Zhongcui Li. The density-based clustering method for privacy-preserving data mining[J]. Mathematical Biosciences and Engineering, 2019, 16(3): 1718-1728. doi: 10.3934/mbe.2019082

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Abstract

Privacy-preserving data mining has become an interesting and emerging issue in recent years since it can, not only hide the sensitive information but still mine the meaningful knowledge at the same time. Since privacy-preserving data mining is a non-trivial task, which is also concerned as a NP-hard problem, several evolutionary algorithms were presented to find the optimized solutions but most of them focus on considering a single-objective function with the pre-defined weight values of three side effects (hiding failure, missing cost, and artificial cost). In this paper, we aim at designing a multiple objective particle swarm optimization method for hiding the sensitive information based on the density clustering approach (named CMPSO). The presented CMPSO is more flexible to select the most appropriate solutions for hiding the sensitive information based on user's preference. Extensive experiments are carried on two datasets to show that the designed CMPSO algorithm has good performance than the traditional single-objective evolutionary approaches in terms of three side effects.

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