Genetic grey wolf optimization and C-mixture for collaborative data publishing  

作　　者：Yogesh R.Kulkarni T.Senthil Murugan 

机构地区：[1]Vel Tech University,Avadi Chennai 600062,Tamil Nadu,India [2]Department of Computer Science and Engineering Vel Tech University Chennai 600062,Tamil Nadu,India

出　　处：《International Journal of Modeling, Simulation, and Scientific Computing》2018年第6期188-210,共23页建模、仿真和科学计算国际期刊（英文）

摘　　要：Data publishing is an area of interest in present day technology that has gained huge attention of researchers and experts.The concept of data publishing faces a lot of security issues,indicating that when any trusted organization provides data to a third party,personal information need not be disclosed.Therefore,to maintain the privacy of the data,this paper proposes an algorithm for privacy preserved collaborative data publishing using the Genetic Grey Wolf Optimizer(Genetic GWO)algorithm for which a C-mixture parameter is used.The C-mixture parameter enhances the privacy of the data if the data does not satisfy the privacy constraints,such as the k-anonymity,l-diversity and the m-privacy.A minimum fitness value is maintained that depends on the minimum value of the generalized information loss and the minimum value of the average equivalence class size.The minimum value of the fitness ensures the maximum utility and the maximum privacy.Experimentation was carried out using the adult dataset,and the proposed Genetic GWO outperformed the existing methods in terms of the generalized information loss and the average equivalence class metric and achieved minimum values at a rate of 0.402 and 0.9,respectively.

关 键 词：K-ANONYMITY l-diversity m-privacy C-mixture Genetic GWO. 

分 类 号：X70[环境科学与工程—环境工程]