期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2013
卷号：10
期号：2
出版社：IJCSI Press
摘要：Free competition in business can be compromised as data mining techniques reveal critical information about business transactions. Hence, there is a need to ensure prevention of disclosures both of confidential personal information which is contextually sensitive. Literature is abounding with state-of-the-art methods for privacy-preserving evolutionary algorithms (EAs) that give solutions to real-world optimization problems. Existing EA solutions are specific to cost function evaluation in privacy-preserving domains. This work proposes implementation of Particle Swarm Optimization (PSO) to locate an optimal generalized feature set. The proposed framework accomplishes k-anonymity by generalization of original dataset.
