<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Data mining on vertically or horizontally partitioned dataset has the overhead of protecting the private data. Perturbation is a technique that protects the revealing of data. This paper proposes a perturbation and anonymization technique that is performed on the vertically partitioned data. A third-party coordinator is used to partition the data recursively in various parties. The parties perturb the data by finding the mean, when the specified threshold level is reached. The perturbation maintains the statistical relationship among attributes.</p></abstract>

Data mining on vertically or horizontally partitioned dataset has the overhead of protecting the private data. Perturbation is a technique that protects the revealing of data. This paper proposes a perturbation and anonymization technique that is performed on the vertically partitioned data. A third-party coordinator is used to partition the data recursively in various parties. The parties perturb the data by finding the mean, when the specified threshold level is reached. The perturbation maintains the statistical relationship among attributes.

Partition Based Perturbation for Privacy Preserving Distributed Data Mining

Computer Science & Engineering, Arunachala College of Engineering

Computer Science & Engineering, Ramco Institute of Technology

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Partition Based Perturbation for Privacy Preserving Distributed Data Mining"}

Data mining on vertically or horizontally partitioned dataset has the overhead of protecting the private data. Perturbation is a technique that protects the...

Partition Based Perturbation for Privacy Preserving Distributed Data Mining

Online veröffentlicht: 26. Juni 2017

Seitenbereich: 44 - 55

DOI: https://doi.org/10.1515/cait-2017-0015

Schlüsselwörter
Vertically partitioned data, perturbation, recursive partitioning, data mining, anonymization

© 2017 M. Antony Sheela et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Partition Based Perturbation for Privacy Preserving Distributed Data Mining

Online veröffentlicht: 26. Juni 2017

Seitenbereich: 44 - 55

DOI: https://doi.org/10.1515/cait-2017-0015

SchlüsselwörterVertically partitioned data, perturbation, recursive partitioning, data mining, anonymization

© 2017 M. Antony Sheela et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Vertically partitioned data, perturbation, recursive partitioning, data mining, anonymization