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2016 ; 2016
(ä): 229-240
Nephropedia Template TP
Proc Int Conf Data Eng
2016[May]; 2016
(ä): 229-240
PMID27616876
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Mining frequent subgraphs from a collection of input graphs is an important topic
in data mining research. However, if the input graphs contain sensitive
information, releasing frequent subgraphs may pose considerable threats to
individual's privacy. In this paper, we study the problem of frequent subgraph
mining (FGM) under the rigorous differential privacy model. We introduce a novel
differentially private FGM algorithm, which is referred to as DFG. In this
algorithm, we first privately identify frequent subgraphs from input graphs, and
then compute the noisy support of each identified frequent subgraph. In
particular, to privately identify frequent subgraphs, we present a frequent
subgraph identification approach which can improve the utility of frequent
subgraph identifications through candidates pruning. Moreover, to compute the
noisy support of each identified frequent subgraph, we devise a lattice-based
noisy support derivation approach, where a series of methods has been proposed to
improve the accuracy of the noisy supports. Through formal privacy analysis, we
prove that our DFG algorithm satisfies ?-differential privacy. Extensive
experimental results on real datasets show that the DFG algorithm can privately
find frequent subgraphs with high data utility.
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