A Hybrid Algorithm for Privacy Preserving in Data Mining
Автор: Sridhar Mandapati, Raveendra Babu Bhogapathi, Ratna Babu Chekka
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 8 vol.5, 2013 года.
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With the proliferation of information available in the internet and databases, the privacy-preserving data mining is extensively used to maintain the privacy of the underlying data. Various methods of the state art are available in the literature for privacy-preserving. Evolutionary Algorithms (EAs) provide effective solutions for various real-world optimization problems. Evolutionary Algorithms are efficiently employed in business practice. In privacy-preserving domain, the existing EA solutions are restricted to specific problems such as cost function evaluation. In this work, it is proposed to implement a Hybrid Evolutionary Algorithm using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Both GA and PSO in the proposed system work with the same population. In the proposed framework, k-anonymity is accomplished by generalization of the original dataset. The hybrid optimization is used to search for optimal generalized feature set.
Privacy-Preserving Data Mining (PPDM), Evolutionary Algorithms (EAs), Swarm Intelligence, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Adult Dataset
Короткий адрес: https://sciup.org/15010453
IDR: 15010453
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