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Privacy protection based on many‐objective optimization algorithm
Author(s) -
Zhang Jiangjiang,
Xue Fei,
Cai Xingjuan,
Cui Zhihua,
Chang Yu,
Zhang Wensheng,
Li Wuzhao
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5342
Subject(s) - computer science , selection (genetic algorithm) , mathematical optimization , optimization problem , differential privacy , process (computing) , population , optimization algorithm , fitness function , privacy protection , private information retrieval , data mining , algorithm , machine learning , mathematics , computer security , genetic algorithm , demography , sociology , operating system
Summary It is difficult to protect users' privacy and to process private information due to the complexity and uncertainty of such information. To protect private information quickly and accurately, a many‐objective optimization algorithm framework based on the hybrid elite selection strategy is proposed in this paper. First, a mating selection mechanism combined with the achievement scale function and angle information index is used to generate elite offspring of the internal population. Then, the balanceable fitness estimation method is employed to select and update the external archive. To test performance, the proposed algorithm is tested on many‐objective optimization problems (MaOPs) and compared with five state‐of‐the‐art algorithms. Experimental simulation results show that the proposed algorithm is more effective in solving MaOPs and can inspire development of a better privacy protection strategy.