Generalizing data to provide anonymity when disclosing information (abstract)
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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Proceedings of the 2007 ACM SIGMOD international conference on Management of data
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IEEE Transactions on Knowledge and Data Engineering
ICDT'05 Proceedings of the 10th international conference on Database Theory
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ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
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IWOCA'10 Proceedings of the 21st international conference on Combinatorial algorithms
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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.