Clustering based Categorical Data Protection
Sowmya S.R1, Manjunath S.S2
1Sowmya S.R, Assistant Professor, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, (Karnataka), India.
2Dr. Manjunath S.S, Professor, Department of Computer Science and Engineering, Academy of Technology& Management Excellence College of Engineering, India.
Manuscript received on 04 December 2019 | Revised Manuscript received on 12 December 2019 | Manuscript Published on 31 December 2019 | PP: 219-221 | Volume-9 Issue-2S December 2019 | Retrieval Number: B11281292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1128.1292S19
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© The Authors. 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: At present, the number of publicly available datasets is increasing day by day. It is therefore imperative to improve the confidentiality of the data, which has become one of the main reasons for an investigation. Extended to provide effective protection techniques that hinder the disclosure of entities in datasets while preserving the usefulness of the data. A systematic approach to categorical data protection is achieved by applying groups to the dataset and then protecting each group. In this paper, we present a survey and analysis on clustering techniques. The analysis of grouping techniques can result in confidential data or outliers in groups, and effective protection methods for such groups.
Keywords: Clustering, Categorical Data, Privacy, Data Mining.
Scope of the Article: Clustering