Event Implementing Privacy of Data and Classification Approach using Data Anonymization Techniques

International Journal of Computer Science and Engineering

© 2016 by SSRG - IJCSE Journal

Volume 3 Issue 12

Year of Publication : 2016

Authors : Mavooru Jyothsna, Mula Sudhakar

10.14445/23488387/IJCSE-V3I12P102

How to Cite?

Mavooru Jyothsna, Mula Sudhakar, "Event Implementing Privacy of Data and Classification Approach using Data Anonymization Techniques," SSRG International Journal of Computer Science and Engineering , vol. 3,  no. 12, pp. 6-9, 2016. Crossref, https://doi.org/10.14445/23488387/IJCSE-V3I12P102

Abstract:

Now a day’s to provide privacy of personal information is a challenging to data management communities. So that the communities are allow to processing of personal information without loss of data. To provide privacy of personal information the communities are using so many data anonymization techniques. One of the data anonymization technique is tree structured data oriented for provide privacy of personal information. In the tree structured data anonymization technique will face the problem of time complexity for generalized data. So that to generalize the personal information it will build the data oriented tree structure and perform the search operation. To overcome this problem we are proposed Posteriori Probability of generalizations approach for performing classification of data. Before performing this process we can store data into database within format cipher format. So that by converting plain format data into cipher format we are using extended tiny encryption algorithm. By implementing encryption process we can provide privacy of personal information and also improve efficiency of stored data. So that by using those operations we can provide privacy of personal information and also get required treatment for particular diseases. In this paper we are take personal information related to the medical data and perform those operations on that data. By implementing those concepts we can overcome time complexity and also provide more security of data.

Keywords:

Cryptography, Anonymization, Generalization, classification, Privacy.

References:

[1].M. Ercan Nergiz Chris Clifton, MultiRelational k- Anonymity": , 2007 IEEE.
[2] L. Sweeney, k-anonymity: a model for protecting privacy ":, International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002; 557-570.
[3] Pierangela Samarati, Member, IEEE Computer Society, “Protecting Respondents Identities in Microdata Release ": , IEEE Transaction on Knowledge and Data Engineering, VOL. 13, NO. 6, Nov/Dec 2001.
[4]. A. Meyerson and R. Williams. On the Complexity of Optimal Kanonymity. In PODS, pages 223–228, 2004.
[5]. Olga Gkountouna, Student Member, IEEE and Manolis Terrovitis, ”Anonymizing Collections of Tree-Structured Data,":IEEE Transaction on Knowledge and Data Engineering, VOL. 27, NO. 8, Aug 2015.
[6]. G. Cormode , Personal privacy vs population privacy: learning to attack anonymization.
[7].R. J. Bayardo and R. Agrawal. Data Privacy through Optimal k-Anonymization. In ICDE, pages 217–228, 2005.
[8]. P. Samarati and L. Sweeney. Generalizing Data to Provide Anonymity when Disclosing Information (abstract). In PODS (see also Technical Report SRICSL- 98-04), 1998.
[9]. G. Ghinita, P. Kalnis, and Y. Tao. Anonymous publication of sensitive transactional data. TKDE, 23(2):161–174, 2011.
[10]. P. Samarati and L. Sweeney. Generalizing Data to Provide Anonymity when Disclosing Information (abstract). In PODS (see also Technical Report SRICSL- 98-04), 1998.