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10.1109/ICDE.2016.7498243 http://scihub22266oqcxt.onion/10.1109/ICDE.2016.7498243 C5015894!5015894!27616876     free     free     free       Proc+Int+Conf+Data+Eng 2016 ; 2016 (ä): 229-40 Nephropedia Template TP {{tp|p=27616876|t=2016. Differentially Private Frequent Subgraph Mining |pdf=|usr=}}{{27616876}} gab.com Text Differentially Private Frequent Subgraph Mining JO: Proc Int Conf Data Eng http://www.kidney.de/pget.php?&tw=1&pmid=27616876 #FOAvip 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. Twit Text FOAVip Differentially Private Frequent Subgraph Mining ????Proc Int Conf Data Eng http://www.kidney.de/pget.php?&tw=1&pmid=27616876 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27616876 #FOAvip English Wikipedia <ref>{{Cite pmid|27616876}}</ref> Differentially Private Frequent Subgraph Mining #MMPMID27616876 Xu S; Su S; Xiong L; Cheng X; Xiao K Proc Int Conf Data Eng 2016[May]; 2016 (ä): 229-40 PMID27616876show ga 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. äDifferentially Private Frequent Subgraph Mining (epmc).pdf DeepDyve Pubget Overpricing