Fixed-parameter tractability and completeness II: on completeness for W[1]
Theoretical Computer Science
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
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Achieving anonymity via clustering
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Approximate algorithms for K-anonymity
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
k-Anonymization with Minimal Loss of Information
IEEE Transactions on Knowledge and Data Engineering
WADS '09 Proceedings of the 11th International Symposium on Algorithms and Data Structures
The k-anonymity problem is hard
FCT'09 Proceedings of the 17th international conference on Fundamentals of computation theory
Resolving the complexity of some data privacy problems
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
ICDT'05 Proceedings of the 10th international conference on Database Theory
Fast algorithms for weighted bipartite matching
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
Parameterized Complexity
On the complexity of the l-diversity problem
MFCS'11 Proceedings of the 36th international conference on Mathematical foundations of computer science
The effect of homogeneity on the complexity of k-anonymity
FCT'11 Proceedings of the 18th international conference on Fundamentals of computation theory
The l-Diversity problem: Tractability and approximability
Theoretical Computer Science
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The problem of publishing personal data without giving up privacy is becoming increasingly important. A precise formalization that has been recently proposed is the k-anonymity, where the rows of a table are partitioned in clusters of size at least k and all rows in a cluster become the same tuple after the suppression of some entries. The natural optimization problem, where the goal is to minimize the number of suppressed entries, is hard even when the stored values are over a binary alphabet or the table consists of a bounded number of columns. In this paper we study how the complexity of the problem is influenced by different parameters. First we show that the problem is W[1]-hard when parameterized by the value of the solution (and k). Then we exhibit a fixed-parameter algorithm when the problem is parameterized by the number of columns and the number of different values in any column.