Computing a maximum cardinality matching in a bipartite graph in time On1.5m/logn
Information Processing Letters
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
Some APX-completeness results for cubic graphs
Theoretical Computer Science
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
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
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
Achieving anonymity via clustering
ACM Transactions on Algorithms (TALG)
Approximate algorithms with generalizing attribute values for k-anonymity
Information Systems
Resolving the complexity of some data privacy problems
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
Anonymizing binary and small tables is hard to approximate
Journal of Combinatorial Optimization
ICDT'05 Proceedings of the 10th international conference on Database Theory
Parameterized Complexity
The effect of homogeneity on the computational complexity of combinatorial data anonymization
Data Mining and Knowledge Discovery
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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 into clusters of sizes 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. Finally, we prove that k-anonymity is still APX-hard even when restricting to instances with 3 columns and k=3.