期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2015
卷号：6
期号：2
页码：1835-1840
出版社：TechScience Publications
摘要：Privacy preserving Data mining is an emerging technology which performs data mining operations in centralized or distributed data in a secured manner to preserve sensitive data. A number of techniques such as randomization, secured sum algorithm and k-anonymity have been suggested in order to perform privacy-preserving data mining. In this paper, a survey on recent researches made on Privacy preserving data mining techniques with Fuzzy logic, neural network learning, secured sum and encryption algorithms is presented. This will enable to understand the challenges faced in Privacy preserving data mining and also helps to identify best techniques suitable for various data environment.
关键词：Multi level Trust Privacy Preserving Data mining;(MLT-PPDM); Neural Network Learning (NNL); Non;negative Matrix Factorization (NMF); Probabilistic Neural;Network (PNN) Privacy Preserving Data Mining (PPDM);Privacy Preserving Data Publishing (PPDP); Secure;Multiparty Computation (SMC)
