International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Contact Us

IJSTR >> Volume 9 - Issue 1, January 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

K-Anonymization Approach FOR Privacy Preserving IN Data Mining

[Full Text]



Vimalkumar B. Vaghela



Anonymization, Data Mining, Generalization, Information Loss, Information Santization, Privacy Preserving, Suppression



Data is collected and processed using diverse sources and tools that lead to privacy issues. Till now randomization, k-anonymization model, l diversity, t closeness, cryptography and many more techniques have been used to preserve the privacy of an individual. But each and every technique have their own demerits i.e. Information Loss, Privacy breached, Low Data Utility. Among all these approach, k anonymization approach one of the mostly used anonymization based approach. However, this approach suffers from the issue of information loss. So, it is challenging task for data miner to mine data. The paper focus to decrease information loss using the 2 level k anonymization approach and also preserve privacy as compared to the traditional approach. The main aim of this approach is to decrease data loss and no compromise with privacy.



[1] Pawan Baladhare and Devesh Jinwala,”Novel approaches for privacy preserving data mining in k- anonymity model” Journal of information science and engineering 32,63-78(2016)
[2] Samarati, Pierangela; Sweeney, Latanya (1998). "Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression" . Harvard Data Privacy Lab. Retrieved April 12, 2017
[3] Y. Lindell and B. Pinkas, “Privacy preserving data mining,” Journal of Cryptology, Vol. 15, 2002, pp. 177-206.
[4] M. Upmanyu, A. M. Namboodiri, K. Srinathan, and C. V. Jawahar, “Efficient privacy preserving k-means clustering,” Intelligent and Security Informatics, LNCS, Vol. 6122, 2010, pp. 154-166.
[5] G. Jagannathan, K. Pillaipakkamnatt, R. N. Wright, and D. Umano, “Communication-efficient privacy preserving clustering,” Transactions on Data Privacy, Vol. 3, 2010, pp. 1-25.
[6] L. Sweeney, “k-Anonymity: a model for protecting privacy,” International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, Vol. 10, 2002, pp. 557-570.
[7] J. W. Byun, A. Kamra, E. Bertino, and N. Li, “Efficient k-anonymization using clustering techniques,” in Proceedings of International Conference on DatabaseSystems for Advanced Applications, 2007, pp. 188-200.
[8] G. Loukides and J. Shao, “Capturing data usefulness and privacy protection in k anonymization,” in Proceedings of ACM Symposium on Applied Computing, 2007, pp. 370-374.
[9] C.-C. Chiu and C.-Y. Tsai, “A k-anonymity clustering method for effective data privacy preservation,” in Proceeding of the 3rd International Conference on AdvancedData Mining and Application, Vol. 4632, 2007, pp. 89-99.
[10] J.-L. Lin and M.-C. Wei, “An Efficient clustering method for k-anonymization,” in Proceeding of International Workshop on Privacy and Anonymity in InformationSociety, 2008, pp. 46-50.
[11] M. E. Kabir, H. Wang and E. Bertino, “Efficient systematic clustering method for k-anonymization,” Acta Informatica, Vol. 48, 2011, pp. 51-66.
[12] X. Xiao and Y. Tao, “Anatomy: simple and effective privacy preservation,” in Proceedings of the 32nd International Conference on Very Large Data Bases, 2006, pp.139-150.
[13] Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam, “l-diversity: privacy beyond k-anonymity,” in Proceedings of the 22nd International Conferenceon Data Engineering, 2006, pp. 1-12.
[14] N. Li, T. Li and S. Venkatasubramanian, “t-closeness: privacy beyond k-anonymity and l-diversity,” International Conference on Data Engineering, 2007, pp. 106-115.
[15] R. C.-W. Wong, J. Li, A.W.-C. Fu, and K. Wang, “(a, k) anonymity: an enhanced k-anonymity model for privacy preserving data publishing,” in Proceedings of the12th ACM SIGKDD International Conference on Knowledge Discovery and DataMining, 2006, pp. 754-759.
[16] J. Goldberger and T. Tassa, “Efficient anonymization with enhanced utility,” Transactions on Data Privacy, Vol. 3, 2010, pp. 149-175.
[17] K. LeFevre, D. J. DeWitt, and R. Ramakrishnan, “Incognito: efficient full domain k-anonymity,” in Proceedings of the ACM SIGMOD International Conference onManagement of Data, 2005, pp. 49-60.
[18] UCI machine learning repository, http://archive.ics.uci.edu/ml/datasets.html.
[19] R. Bayardo and R. Agrawal, “Data privacy through optimal k-anonymization,” in Proceedings of the 21st International Conference on Data Engineering, 2005, pp. 217-228.
[20] Analysis of Various Sentiment Classification Techniques
[21] Sentiment Analysis using Support Vector Machine based on Feature Selection and Semantic Analysis
[22] Similarity Measures for Collaborative Filtering to Alleviate the New User Cold Start Problem
[23] An Algorithmic Approach for Recommendation of Movie Under a New User Cold Start Approach
[24] Privacy Preserving by Anonymization Approach
[25] Vaghela, Vimalkumar B., and Bhumika M. Jadav. "Analysis of various sentiment classification techniques." International Journal of Computer Applications 140.3 (2016).