Main Reference PaperPrivacy Preserving Decision Tree Learning Using Unrealized Data Sets IEEE Transactions on Knowledge and Data Engineering, February 2012.
  • Privacy of the data set is protected through the reforming of original data set into set of unrealized data set while achieving utility by the possibility of generating decision tree from the unrealized data set too. Original data set can be reconstructed only if entire unrealized data set is available.

+ Description
  • Privacy of the data set is protected through the reforming of original data set into set of unrealized data set while achieving utility by the possibility of generating decision tree from the unrealized data set too. Original data set can be reconstructed only if entire unrealized data set is available.

  • To protect the privacy of data set

  • To achieve utility through the generation of decision tree using the unrealized data set

+ Aim & Objectives
  • To protect the privacy of data set

  • To achieve utility through the generation of decision tree using the unrealized data set

  • If all the samples are stolen by the attacker, it is possible to reconstruct the original data set. Hence to improve the security further,cryptographic approach of preserving privacy is enforced over the data set.

+ Contribution
  • If all the samples are stolen by the attacker, it is possible to reconstruct the original data set. Hence to improve the security further,cryptographic approach of preserving privacy is enforced over the data set.

  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1, J2SE

+ Software Tools & Technologies
  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1, J2SE

  • B.E / B.Tech / M.E / M.Tech

+ Project Recommended For
  • B.E / B.Tech / M.E / M.Tech

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