From the experts and researchers, data publishing is the center of attention in the latest technology, which receives great interest. The idea of data publishing faces a large number of security problems chiefly, while any trusted organization presents data to the third party, personal information requires not to be revealed. Hence, to keep the data privacy, this work presents a method for privacy preserved collaborative data publishing by exploiting the Weed and Particle Swarm Optimization algorithm (W-PSO) for that a C-mixture parameter is utilized. The parameter of C-mixture improves data privacy if the data does not assure privacy constraints, like l -diversity, m -privacy and k -anonymity. The least fitness value is controlled which is based upon the least value of the widespread information loss and the least value of the average equivalence class size. The minimum value of the fitness assures the utmost utility and the least privacy. Simulation is performed by exploiting the adult dataset and the proposed method is superior to the conventional algorithms regarding the widespread information loss and the average equivalence class metric and attained minimum values.
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