18,157 research outputs found
On the complexity of computing the -restricted edge-connectivity of a graph
The \emph{-restricted edge-connectivity} of a graph , denoted by
, is defined as the minimum size of an edge set whose removal
leaves exactly two connected components each containing at least vertices.
This graph invariant, which can be seen as a generalization of a minimum
edge-cut, has been extensively studied from a combinatorial point of view.
However, very little is known about the complexity of computing .
Very recently, in the parameterized complexity community the notion of
\emph{good edge separation} of a graph has been defined, which happens to be
essentially the same as the -restricted edge-connectivity. Motivated by the
relevance of this invariant from both combinatorial and algorithmic points of
view, in this article we initiate a systematic study of its computational
complexity, with special emphasis on its parameterized complexity for several
choices of the parameters. We provide a number of NP-hardness and W[1]-hardness
results, as well as FPT-algorithms.Comment: 16 pages, 4 figure
On the Complexity of -Closeness Anonymization and Related Problems
An important issue in releasing individual data is to protect the sensitive
information from being leaked and maliciously utilized. Famous privacy
preserving principles that aim to ensure both data privacy and data integrity,
such as -anonymity and -diversity, have been extensively studied both
theoretically and empirically. Nonetheless, these widely-adopted principles are
still insufficient to prevent attribute disclosure if the attacker has partial
knowledge about the overall sensitive data distribution. The -closeness
principle has been proposed to fix this, which also has the benefit of
supporting numerical sensitive attributes. However, in contrast to
-anonymity and -diversity, the theoretical aspect of -closeness has
not been well investigated.
We initiate the first systematic theoretical study on the -closeness
principle under the commonly-used attribute suppression model. We prove that
for every constant such that , it is NP-hard to find an optimal
-closeness generalization of a given table. The proof consists of several
reductions each of which works for different values of , which together
cover the full range. To complement this negative result, we also provide exact
and fixed-parameter algorithms. Finally, we answer some open questions
regarding the complexity of -anonymity and -diversity left in the
literature.Comment: An extended abstract to appear in DASFAA 201
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