Main Reference PaperPrivacy Preserving Decision Tree Learning Using Unrealized Data Sets IEEE Transactions on Knowledge and Data Engineering, February 2012.
  • This paper introduces a privacy preserving approach that can be applied to decision tree learning, without concomitant loss of accuracy. It describes an approach to the preservation of the privacy of collected data samples in cases where information from the sample database has been partially lost. This approach converts the original sample data sets into a group of unreal data sets, from which the original samples cannot be reconstructed without the entire group of unreal data sets. Meanwhile, an accurate decision tree can be built directly from those unreal data sets. This novel approach can be applied directly to the data storage as soon as the first sample is collected. The approach is compatible with other privacy preserving approaches, such as cryptography, for extra protection.

+ Description
  • This paper introduces a privacy preserving approach that can be applied to decision tree learning, without concomitant loss of accuracy. It describes an approach to the preservation of the privacy of collected data samples in cases where information from the sample database has been partially lost. This approach converts the original sample data sets into a group of unreal data sets, from which the original samples cannot be reconstructed without the entire group of unreal data sets. Meanwhile, an accurate decision tree can be built directly from those unreal data sets. This novel approach can be applied directly to the data storage as soon as the first sample is collected. The approach is compatible with other privacy preserving approaches, such as cryptography, for extra protection.

  • 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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