A Hybrid Algorithm for Privacy Preserving in Data Mining

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Sridhar Mandapati 1,* Raveendra Babu Bhogapathi 2 Ratna Babu Chekka 3

1. Dept. of Computer Applications, R.V.R & J.C College of Engineering, Guntur, India

2. Dept. of Computer Science and Engineering, VNR VJIET, Hyderabad, India

3. Dept. of Computer Science and Engineering, R.V.R & J.C College of Engineering, Guntur, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2013.08.06

Received: 7 Oct. 2012 / Revised: 11 Feb. 2013 / Accepted: 4 May 2013 / Published: 8 Jul. 2013

Index Terms

Privacy-Preserving Data Mining (PPDM), Evolutionary Algorithms (EAs), Swarm Intelligence, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Adult Dataset


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.

Cite This Paper

Sridhar Mandapati, Raveendra Babu Bhogapathi, Ratna Babu Chekka, "A Hybrid Algorithm for Privacy Preserving in Data Mining", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.8, pp.47-53, 2013. DOI:10.5815/ijisa.2013.08.06


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