Abstract
The problem of publishing personal data without giving up privacy is becoming increasingly important. An interesting formalization recently proposed is the k-anonymity. This approach requires that the rows in a table are clustered in sets of size at least k and that all the rows in a cluster are related to the same tuple, after the suppression of some records. The problem has been shown to be NP-hard when the values are over a ternary alphabet, k = 3 and the rows length is unbounded. In this paper we give a lower bound on the approximation of two restrictions of the problem, when the records values are over a binary alphabet and k = 3, and when the records have length at most 8 and k = 4, showing that these restrictions of the problem are APX-hard.
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References
Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: Anonymizing Tables. In: Eiter, T., Libkin, L. (eds.) ICDT 2005. LNCS, vol. 3363, pp. 246–258. Springer, Heidelberg (2004)
Alimonti, P., Kann, V.: Some APX-Completeness Results for Cubic Graphs. Theoretical Computer Science 237(1-2), 123–134 (2000)
Ausiello, G., Crescenzi, P., Gambosi, G., Kann, V., Marchetti-Spaccamela, A., Protasi, M.: Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties. Springer, Heidelberg (1999)
Gionis, A., Tassa, T.: k-Anonymization with Minimal Loss of Information. IEEE Trans. Knowl. Data Eng. 21(2), 206–219 (2009)
Park, H., Shim, K.: Approximate algorithms for k-Anonymity. In: Chan, C.Y., Ooi, B.C., Zhou, A. (eds.) ACM SIGMOD International Conference on Management of Data, pp. 67–78. ACM Press, New York (2007)
Samarati, P.: Protecting Respondents’ Identities in Microdata Release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)
Samarati, P., Sweeney, L.: Generalizing Data to Provide Anonymity When Disclosing Information (Abstract). In: Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, p. 188. ACM Press, New York (1998)
Sweeney, L.: k-Anonymity: a Model for Protecting Privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)
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Bonizzoni, P., Della Vedova, G., Dondi, R. (2009). The k-Anonymity Problem Is Hard. In: Kutyłowski, M., Charatonik, W., Gębala, M. (eds) Fundamentals of Computation Theory. FCT 2009. Lecture Notes in Computer Science, vol 5699. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03409-1_4
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DOI: https://doi.org/10.1007/978-3-642-03409-1_4
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