Main Reference PaperDifferential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing, IEEE Transactions on Big Data, 2018 [Python]
  • To find an intruders in wireless big data, this work is proposed a machine learning strategy using differential privacy. To guarantee the privacy protection by adding Laplace mechanisms, and design two different algorithms Output Perturbation (OPP) and Objective Perturbation (OJP), which satisfy differential privacy.

Description
  • To find an intruders in wireless big data, this work is proposed a machine learning strategy using differential privacy. To guarantee the privacy protection by adding Laplace mechanisms, and design two different algorithms Output Perturbation (OPP) and Objective Perturbation (OJP), which satisfy differential privacy.

  • To achieve high quality privacy preserving of data.

Aim & Objectives
  • To achieve high quality privacy preserving of data.

  • A technique is contributed that improves the accuracy of the proposed scheme.

Contribution
  • A technique is contributed that improves the accuracy of the proposed scheme.

  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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