1 research outputs found
Parameterized Complexity of the k-anonymity Problem
The problem of publishing personal data without giving up privacy is becoming
increasingly important. An interesting formalization that has been recently
proposed is the -anonymity. This approach requires that the rows of a table
are partitioned in clusters of size at least and that all the 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 known to be APX-hard even when the records values are
over a binary alphabet and , and when the records have length at most 8
and . In this paper we study how the complexity of the problem is
influenced by different parameters. In this paper we follow this direction of
research, first showing that the problem is W[1]-hard when parameterized by the
size of the solution (and the value ). Then we exhibit a fixed parameter
algorithm, when the problem is parameterized by the size of the alphabet and
the number of columns. Finally, we investigate the computational (and
approximation) complexity of the -anonymity problem, when restricting the
instance to records having length bounded by 3 and . We show that such a
restriction is APX-hard.Comment: 22 pages, 2 figure