27,998 research outputs found
Approximate Two-Party Privacy-Preserving String Matching with Linear Complexity
Consider two parties who want to compare their strings, e.g., genomes, but do
not want to reveal them to each other. We present a system for
privacy-preserving matching of strings, which differs from existing systems by
providing a deterministic approximation instead of an exact distance. It is
efficient (linear complexity), non-interactive and does not involve a third
party which makes it particularly suitable for cloud computing. We extend our
protocol, such that it mitigates iterated differential attacks proposed by
Goodrich. Further an implementation of the system is evaluated and compared
against current privacy-preserving string matching algorithms.Comment: 6 pages, 4 figure
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
The Internet has enabled the creation of a growing number of large-scale
knowledge bases in a variety of domains containing complementary information.
Tools for automatically aligning these knowledge bases would make it possible
to unify many sources of structured knowledge and answer complex queries.
However, the efficient alignment of large-scale knowledge bases still poses a
considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a
simple algorithm for aligning knowledge bases with millions of entities and
facts. SiGMa is an iterative propagation algorithm which leverages both the
structural information from the relationship graph as well as flexible
similarity measures between entity properties in a greedy local search, thus
making it scalable. Despite its greedy nature, our experiments indicate that
SiGMa can efficiently match some of the world's largest knowledge bases with
high precision. We provide additional experiments on benchmark datasets which
demonstrate that SiGMa can outperform state-of-the-art approaches both in
accuracy and efficiency.Comment: 10 pages + 2 pages appendix; 5 figures -- initial preprin
Average-Case Optimal Approximate Circular String Matching
Approximate string matching is the problem of finding all factors of a text t
of length n that are at a distance at most k from a pattern x of length m.
Approximate circular string matching is the problem of finding all factors of t
that are at a distance at most k from x or from any of its rotations. In this
article, we present a new algorithm for approximate circular string matching
under the edit distance model with optimal average-case search time O(n(k + log
m)/m). Optimal average-case search time can also be achieved by the algorithms
for multiple approximate string matching (Fredriksson and Navarro, 2004) using
x and its rotations as the set of multiple patterns. Here we reduce the
preprocessing time and space requirements compared to that approach
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