Heuristics for privacy preserving data mining: An evaluation
S. Mohana, S. A. Sahaaya Arul Mary · 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET) · 2017
Availability of information in profusion in the internet and databases is common knowledge. It has to be viewed in the backdrop of chances for disclosure of such information by a third party. Privacy Preserving Data Mining (PPDM) is in use for maintaining the privacy of individuals. Numerous updated methods are available for the purpose. Evolutionary Algorithms (EA's) are able to provide effective solutions for real-world optimization problems. They find use in business practice too. This work has a proposal for the implementation of an EA using K-Anonymization; particle swam optimization (PSO), Ant colony optimization (ACO) and a Genetic Algorithm (GA). We herein propose Genetic algorithm and particle swam optimization work with the same data. The use of generalization of the original dataset is meant for achieving K-anonymity. A collection of people called “chromosomes” frame the populace which shows an aggregate solution for a characterized issue in the proposed GA. The achievement of good accuracy is obtained by GA optimization, recall and precision in comparison with K-Anonymization, PSO and ACO methods.