Privacy Preserving Data Mining using Optimization Methods
(Sprache: Englisch)
PPDM using optimization methods brings you up-to-date with various PPDM Algorithms, Randomization Method, Group Based Anonymization, Distributed Privacy-Preserving Data Mining and k-Anonymous Data Mining discussed. The performance of classification accuracy...
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PPDM using optimization methods brings you up-to-date with various PPDM Algorithms, Randomization Method, Group Based Anonymization, Distributed Privacy-Preserving Data Mining and k-Anonymous Data Mining discussed. The performance of classification accuracy and the computational time of various data mining algorithms with and without anonymized datasets and also model ranking algorithms are discussed. This book explores the possibility of using fuzzy logic for anonymization of data. The anonymization achieved is evaluated for classification accuracy using data mining algorithms. The state-of-the-art methods for privacy-preserving evolutionary algorithms (EAs) are discussed. A Hybrid Evolutionary Algorithm using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are discussed.
Bibliographische Angaben
- Autor: Sridhar Mandapati
- 2014, 128 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 3659542628
- ISBN-13: 9783659542626
Sprache:
Englisch
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