A Multiple Objective PSO-based Approach for Data Sanitization

被引：3

作者：

Lin, Jerry Chun-Wei ^{[1
,2
]}

Zhang, Yuyu ^{[1
]}

Chen, Chun-Hao ^{[3
]}

Wu, Jimmy Ming-Tai ^{[4
]}

Chen, Chien-Ming ^{[1
]}

Hong, Tzung-Pei ^{[5
]}

机构：

[1] Harbin Inst Technol Shenzhen, Shenzhen, Peoples R China

[2] Western Norway Univ Appl Sci, Bergen, Norway

[3] Tamkang Univ, New Taipei, Taiwan

[4] Shandong Univ Technol, Zibo, Shandong, Peoples R China

[5] Natl Univ Kaohsiung, Kaohsiung, Taiwan

来源：

2018 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI) | 2018年

基金：

中国国家自然科学基金;

关键词：

optimization; privacy-preserving and security; data sanitization; multiple objective; ALGORITHMS;

D O I：

10.1109/TAAI.2018.00039

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a multi-objective particle swarm optimization (MOPSO)-based framework is presented to find the multiple solutions rather than a single one. The presented grid-based algorithm is used to assign the probability of the non-dominated solution for next iteration. Based on the designed algorithm, it is unnecessary to pre-define the weights of the side effects for evaluation but the non-dominated solutions can be discovered as an alternative way for data sanitization. Extensive experiments are carried on two datasets to show that the designed grid-based algorithm achieves good performance than the traditional single-objective evolution algorithms.

引用

页码：148 / 151

页数：4

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