Implementing A Hybrid Slicing Approach for Privacy Preserving Data Publishing
Vikas Baisane, N. D. Kamble
1Vikas Baisane, D.N. Patel College of Engineering, Shahada, Dist: Nandurbar (Maharashtra), India.
2Mr. N. D. Kamble, Asst. Prof., Shreeyash College of Engineering and Technology, Education: ME (CSE), Aurangabad (Maharashtra), India.
Manuscript received on September 18, 2017. | Revised Manuscript received on September 22, 2017. | Manuscript published on July 25, 2017. | PP: 44-55 | Volume-4 Issue-12, July 2017. | Retrieval Number: L12020741217
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Several techniques of anonymity, such as generalization and disruption, have been designed to protect privacy from the publication of micro-data. Recent work has shown that generalization loses much information, especially for high dimensional data. Bucketization, on the other hand, does not prevent the disclosure of membership and does not apply to data that do not have a clear separation between quasi-identifiable attributes and sensitive attributes. In this paper, we present a new technique called overlapping slice, which divides the data horizontally and vertically. We show that the overlap section preserves a better data utility than generalization and can be used for the protection of belonging to belonging. Another important advantage of the overlap slice is that it can handle large data storage.
Keywords: Privacy preservation, data anonymization, data publishing, data security