Privacy Preserving in Data Mining with No Data Loss with a Combinational Scheme
A. Damodar1, C. Rajeev2, M. Srinivas Reddy3
1A. Damodar, Assistant Professor, Department of CSE, Malla Reddy Engineering College for Women, Hyderabad (Telangana), India.
2C. Rajeev, Assistant Professor, Department of CSE, Malla Reddy Engineering College for Women, Hyderabad (Telangana), India.
3M. Srinivas Reddy, Assistant Professor, Department of CSE, Malla Reddy Engineering College for Women, Hyderabad (Telangana), India.
Manuscript received on 10 December 2019 | Revised Manuscript received on 23 December 2019 | Manuscript Published on 31 December 2019 | PP: 457-459 | Volume-8 Issue-4S3 December 2019 | Retrieval Number: D11071284S319/2019©BEIESP | DOI: 10.35940/ijrte.D1107.1284S319
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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: Large amounts of data collected by many organizations under-goes data mining for various purposes like analysis and prediction. During data mining tasks, the sensitive information may be losing its privacy. Hence, Privacyprotection or preservation is becomes major issue for the organizations. Publishing data or sharing information for mining with Privacypreservation is possible through Privacypreserve data mining technique (PPDM). Existing techniques are not able to withstand for some attacks and some suffers with data misfortune. In our paper we conventional an effective and combinational approach for security safeguarding in information mining. Our approach with can withstand from different kinds of assaults and limits data misfortune and increases data re-usability with data reconstruction capability.
Keywords: Privacy Preserving, Sensitive Information, Data Mining, K-anonymity, Randomization.
Scope of the Article: Data Mining