Abstract
Sharing microdata tables is a primary concern in today information society. Privacy issues can be an obstacle to the free flow of such information. In recent years, disclosure control techniques have been developed to modify microdata tables in order to be anonymous. The k-anonymity framework has been widely adopted as a standard technique to remove links between public available identifiers (such as full names) and sensitive data contained in the shared tables. In this paper we give a weaker definition of k-anonymity, allowing lower distortion on the anonymized data. We show that, under the hypothesis in which the adversary is not sure a priori about the presence of a person in the table, the privacy properties of k-anonymity are respected also in the weak k -anonymity framework. Experiments on real-world data show that our approach outperforms k-anonymity in terms of distortion introduced in the released data by the algorithms to enforce anonymity.
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References
Samarati, P., Sweeney, L.: Generalizing data to provide anonymity when disclosing information (abstract). In: Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of Database Systems (PODS), p. 188. ACM Press, New York (1998)
Sweeney, L.: k-Anonymity: a model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10(5), 557–570 (2002)
Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Transactions on Knowledge and Data Engineering (TKDE) 13(6), 1010–1027 (2001)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10(5), 571–588 (2002)
Domingo-Ferrer, J., Mateo-Sanz, J.M.: Practical data-oriented microaggregation for statistical disclosure control. IEEE Transactions on Knowledge and Data Engineering (TKDE) 14(1), 189–201 (2002)
Meyerson, A., Williams, R.: On the complexity of optimal k-anonymity. In: Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS), pp. 223–228 (2004)
Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: Approximation algorithms for k-anonymity. Journal of Privacy Technology 1(2005112001) (2005)
Bayardo, R., Agrawal, R.: Data privacy through optimal k-anonymization. In: Proceedings of the 21st International Conference on Data Engineering (ICDE), Washington, DC, USA, pp. 217–228 (2005)
Aggarwal, C.C.: On k-anonymity and the curse of dimensionality. In: Proceedings of the 31st International Conference on Very Large Databases (VLDB), Trondheim, Norway, VLDB Endowment (2005)
Øhrn, A., Ohno-Machado, L.: Using boolean reasoning to anonymize databases. Artificial Intelligence in Medicine 15(3), 235–254 (1999)
Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-Diversity: Privacy beyond k-anonymity. In: Proceedings of the 22nd IEEE International Conference on Data Engineering (ICDE), Atlanta, GA, USA (2006)
Hettich, S., Bay, S.: The UCI KDD Archive (1999), http://kdd.ics.uci.edu/
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© 2006 Springer-Verlag Berlin Heidelberg
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Atzori, M. (2006). Weak k-Anonymity: A Low-Distortion Model for Protecting Privacy. In: Katsikas, S.K., López, J., Backes, M., Gritzalis, S., Preneel, B. (eds) Information Security. ISC 2006. Lecture Notes in Computer Science, vol 4176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11836810_5
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DOI: https://doi.org/10.1007/11836810_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38341-3
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