A Study of Privacy Preserving Using Anonymization Techniques
DOI:
https://doi.org/10.51983/ajcst-2019.8.S2.2029Keywords:
Privacy, Anonymization, Suppression, Generalization, K-AnonymityAbstract
Now a day’s there is an extensive use of technology that has led to a massive increase in the amount of data that is generated. The analysis of such information will help the business and organization in various ways and also contributing beneficially to society in many different fields. As this data also contains the considerable amount of user-sensitive and private information, it will lead to the potential threats to the user’s privacy if the data is published without applying any privacy preserving techniques to the data. This paper discusses the various anonymization techniques such as generalization and suppression which are used to preserve privacy during data publishing.
References
D. Agrawal and C.C. Aggrawal, "On the design and quantification of privacy-preserving data algorithm," in Proceedings of the twentieth ACM PODS, 2001.
M. Kantarcioglau, J. Jin, and C. Clito, "When do data mining results violate privacy?" in Proceedings of the tenth ACM SIGKDD, 2004.
B. Fung, K. Wang, R. Chen, and P.S. Yu, "Privacy-preserving data publishing: A survey of recent development," ACM Computer Survey (CSUR), vol. 42, no. 4, pp. 14, 2010.
A. Meyerson and R. Williams, "On the complexity of optimal k-anonymity," in Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 223-228, ACM, 2004.
Disha Dubli and D.K. Yadavi, "Secure Techniques of Data Anonymization for Privacy Preservation," International Journal of Advanced Research in Computer Science, vol. 8, no. 5, May-June. 2017.
L. Sweeney, "Achieving k-anonymity privacy protection using generalization and suppression," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 05, pp. 571-588, 2002.
J. Goldberger and T. Tassa, "Efficient anonymizations with enhanced utility," in Data Mining Workshops, 2009. ICDMW’09. IEEE International Conference on, IEEE, pp. 106-113, 2009.
Kinjal Parmar and Vinita Shah, "A Review on Data Anonymization in Privacy Preserving Data Mining," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), vol. 5, no. 2, February. 2016.
D. Samarati and L. Sweeney, "Protecting privacy when disclosing information: Anonymity and its enforcement through generalization and suppression," SRI International, SRI-CSL-98-04, 1998.
L. Sweeney, "Achieving k-anonymity Privacy Protection using generalization and suppression," International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, vol. 10, no. 05, pp. 571-588, 2002.
S.K. Adusumalli and V.V. Kumari, "Attribute-based anonymity for preserving privacy," in Advances in Computing and Communications, pp. 572-579, Springer, 2011.
B. Bercic and C. George, "Identifying Personal Data Using Relational Database Design Principles," International Journal of Law and Information Technology, vol. 17, no. 3, pp. 233-251, 2009.
K. El Emam et al., "A Globally Optimal K-anonymity method for the de-identification of health data," Journal of the American Medical Informatics Association, vol. 16, no. 5, pp. 670-682, 2009.
E. Bertino and R. Sandhu, "Database security – Concepts, approaches, and challenges," IEEE Transactions on Dependable and Secure Computing, vol. 2, no. 1, pp. 2–19, 2005.
M. Reed, P. Syverson, and D. Goldschlag, "Anonymous connections and Onion routing," IEEE Journal of Selected Areas in Communications, vol. 16, no. 4, pp. 482–494, 1998.
M.K. Reiter and A. Rubin, "Crowds: anonymity with Web transactions," ACM Transactions on Information and System Security (TISSEC), vol. 1, no. 1, pp. 66–92, 1998.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 The Research Publication
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
