Paper Title : Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing
ISSN : 2394-2231
Year of Publication : 2021
MLA Style: S. Sambasivam MCA., M.Phil.,, D. Guhan "Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
APA Style: S. Sambasivam MCA., M.Phil.,, D. Guhan "Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
Implement a machine learning strategy for smart edges using differential privacy. In existing system focus attention on privacy protection in training datasets in wireless big data scenario and it also adding Laplace mechanisms, and design two different algorithms are Output Perturbation (OPP) and Objective Perturbation (OJP). Privacy Preserving issues presented in the existing literatures for differential privacy in the correlated datasets, and further provided differential privacy preserving methods for correlated datasets, guaranteeing privacy by theoretical deduction. Its processes have been developed to cleanse private information from the samples while keeping their utility and it can approach that can be applied to decision tree learning, without connected loss of accuracy.
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—— Privacy Preserving, Machine Learning, Perturbation, Wireless Big data, Laplacian Mechanism.