Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33122
Balanced k-Anonymization
Authors: Sabah S. Al-Fedaghi
Abstract:
The technique of k-anonymization has been proposed to obfuscate private data through associating it with at least k identities. This paper investigates the basic tabular structures that underline the notion of k-anonymization using cell suppression. These structures are studied under idealized conditions to identify the essential features of the k-anonymization notion. We optimize data kanonymization through requiring a minimum number of anonymized values that are balanced over all columns and rows. We study the relationship between the sizes of the anonymized tables, the value k, and the number of attributes. This study has a theoretical value through contributing to develop a mathematical foundation of the kanonymization concept. Its practical significance is still to be investigated.Keywords: Balanced tables, k-anonymization, private data
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1085359
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1226References:
[1] S. S. Al-Fedaghi,, "A systematic approach to anonymity," Proceedings of 3rd International Workshop on Security in Information Systems WOSIS-2005, Miami, May, 2005.
[2] S. S. Al-Fedaghi., G. Fiedler, and B. Thalheim "Privacy enhanced information systems," Proceedings of The 15th European-Japanese Conference on Information Modelling And Knowledge Bases, Tallinn, Estonia, 2005.
[3] G. T. Aggarwal, G., K. Feder, R. Kenthapadi, R. Motwani, D. Panigrahy, D. Thomas, A. Zhu, "k-anonymity: algorithms and hardness," 2004, http://dbpubs.stanford.edu:8090/pub/2004-24.
[4] R. J. Bayardo and R. Agrawal, "Data privacy through optimal kanonymization" Proc. of ICDE-2005, 2005.
[5] G. Duncan, and D. Lambert, "The risk of disclosure for microdata," Journal of Business & Economic Statistics," 7, 1989, pp. 207-217.
[6] J. S. González., "Improving cell suppression in statistical disclosure control," Conference of European Statisticians, Skopje, 14-16 March 2001 http://www.unece.org/stats/documents/2001/03/confidentiality/16.e.pdf
[7] A. Meyerson, and R. Williams, "On the complexity of optimal kanonymity," PODS 2004 June 1416, 2004, Paris, France.
[8] L. Sweeney, "K-anonymity: a model for protecting privacy," International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002; 557-570.
[9] P. Samarati, "Protecting respondents' identities in microdata release.," IEEE Transactions on Knowledge and Data Engineering, 13(6), November/December 2001.
[10] S. Zhong, Z. Yang, and R. N. Wright, "Privacy enhancing kanonymization of customer data," PODS 2005 June 1315, 2005, Baltimore, Maryland. http://www.almaden.ibm.com/cs/people/bayardo/ps/icde05.pdf
[11] A. Hundpool and L. Willenborg, "Mu-argus and tau argus: software for statistical disclosure control," Third Int-l Seminar on Statistical Confidentiality, 1996.
[12] A. Meyerson and R. Williams, "On the complexity of optimal kanonymity," In Proc. of the 23rd ACM SIGMOD-SIGACT-SIGART Symposium on the Principles of Database Systems, 223-228, 2004.
[13] E. Bertino, C. O. Beng, Y. Yanjiang, and R. H. Deng, "Privacy and ownership preserving of outsourced medical data," 2005 International Conference on Data Engineering (ICDE), Tokyo, Japan, http://wwwscf. usc.edu/~csci586/paper/icde05.pdf.
[14] L. Sweeney, "Achieving k-anonymity privacy protection using generalization and suppression," Int-l Journal on Uncertainty, Fuzziness, and Knowledge-Base Systems 10(5): 571-588, 2002.
[15] L. Sweeney, "Datafly: a system for providing anonymity in medical data. In Database Security XI: Status and Prospects," IFIP TC11 WG11.3 11th Int-l Conf. on Database Security, 356-381, 1998.