Parameterized complexity of k-anonymity: hardness and tractability

Paola Bonizzoni (), Gianluca Della Vedova (), Riccardo Dondi () and Yuri Pirola ()
Additional contact information
Paola Bonizzoni: Univ. Milano-Bicocca
Gianluca Della Vedova: Univ. Milano-Bicocca
Riccardo Dondi: Università degli Studi di Bergamo
Yuri Pirola: Univ. Milano-Bicocca

Journal of Combinatorial Optimization, 2013, vol. 26, issue 1, No 3, 19-43

Abstract: Abstract 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.

Keywords: Anonymity; Fixed-parameter complexity; Approximation algorithms; Hardness (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10878-011-9428-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcomop:v:26:y:2013:i:1:d:10.1007_s10878-011-9428-9

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10878

DOI: 10.1007/s10878-011-9428-9

Access Statistics for this article

Journal of Combinatorial Optimization is currently edited by Thai, My T.

More articles in Journal of Combinatorial Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().