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Data mining is a process to extract useful knowledge from large amounts of data. To conduct data mining, we often need to collect data. However, privacy concerns may prevent people from sharing the data and some types of information about the data. How we conduct data mining without breaching data privacy presents a challenge.
Secure Data Mining provides solutions to the problem of data mining without compromising data privacy. This professional book is designed for practitioners and researchers in industry, as well as a secondary textbook for advanced-level students in computer science.
Table des matières
Preface.- Introduction.- Literature Review.- Fundamental Security and Privacy.- Privacy-Preserving Association Rule Mining.- Privacy-Preserving Sequential Pattern Mining.- Privacy-Preserving Naive Bayesian Classification.- Privacy-Preserving Decision Tree Classification.- Privacy-Preserving k-Nearest Neighbor Classification.- Privacy-Preserving Support Vector Machine Classification.- Privacy-Preserving k-Mean Clustering.- Privacy-Preserving k-Medoids Clustering.- Other Selected Topics.- Conclusion and Future Work.- Index.
Résumé
Data mining is a process to extract useful knowledge from large amounts of data. To conduct data mining, we often need to collect data. How we conduct data mining without breaching data privacy presents a challenge. Secure Data Mining provides solutions to the problem of data mining without compromising data privacy.
