Main Reference PaperAnonymization of Sensitive Quasi-Identifiers for l-diversity and t-closeness, IEEE Transactions on Dependable and Secure Computing, April 2017 [python]
  • Novel privacy models are proposed that are l-diversity, t-closeness and a method that handles sensitive quasi identifiers (S-QID). These models form two algorithms anonymization and reconstruction that is used by the data analyzer.

Description
  • Novel privacy models are proposed that are l-diversity, t-closeness and a method that handles sensitive quasi identifiers (S-QID). These models form two algorithms anonymization and reconstruction that is used by the data analyzer.

  • To consider the feature of sensitive and quasi identifiers in every in all attributes.

  • To utilize anonymization and reconstruction algorithm.

Aim & Objectives
  • To consider the feature of sensitive and quasi identifiers in every in all attributes.

  • To utilize anonymization and reconstruction algorithm.

  • A technique is contributed that preserves information gain.

Contribution
  • A technique is contributed that preserves information gain.

  • 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-Depending on the complexity of the project and requirements.

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

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