经过预处理之后的明文文本 (只保留字符集中的字符) differentialprivacyisthestateoftheartgoalfortheproblemofprivacypreservingdatarelease andprivacypreservingdataminingexistingtechniquesusingdifferentialprivacyhoweverca nnoteffectivelyhandlethepublicationofhighdimensionaldatainparticularwhentheinputd atasetcontainsalargenumberofattributesexistingmethodsincurhighercomputingcomple xityandlowerinformationtonoiseratiowhichrendersthepublisheddatanexttouselessthisp roposalaimstoreducecomputingcomplexityandsignaltonoiseratiothestartingpointistoap proximatethefulldistributionofhighdimensionaldatasetwithasetoflowdimensionalmargi naldistributionsviaoptimizingscorefunctionandreducingsensitivityinwhichgenerationof noisyconditionaldistributionswithdifferentialprivacyiscomputedinasetoflowdimensiona Isubspacesandthenthesampletuplesfromthenoisyapproximationdistributionareusedto generateandreleasethesyntheticdatasetsomecrucialscienceproblemswouldbeinvestiga tedbelowiconstructingalowkdegreebayesiannetworkoverthehighdimensionaldatasetvi aexponentialmechanismindifferentialprivacywherethescorefunctionisoptimizedtoredu cethesensitivityusingmutualinformationequivalenceclassesinmaximumjointdistributio nanddynamicprogrammingiistudyingthealgorithmtocomputeasetofnoisyconditionaldis tributionsfromjointdistributionsinthesubspaceofbayesiannetworkviathelaplacemechan ismofdifferentialprivacyiiiexploringhowtogeneratesyntheticdatafromthedifferentiallypr ivatebayesiannetworkandconditionaldistributionswithoutexplicitlymaterializingthenois yglobaldistributiontheproposedsolutionmayhavetheoreticalandtechnicalsignificancefo rsyntheticdatagenerationwithdifferentialprivacyonbusinessprospects经过预处理之后的明文文本 (只保留字符集中的字符) differentialprivacyisthestateoftheartgoalfortheproblemofprivacypreservingdatarelease andprivacypreservingdataminingexistingtechniquesusingdifferentialprivacyhoweverca nnoteffectivelyhandlethepublicationofhighdimensionaldatainparticularwhentheinputd atasetcontainsalargenumberofattributesexistingmethodsincurhighercomputingcomple xityandlowerinformationtonoiseratiowhichrendersthepublisheddatanexttouselessthisp roposalaimstoreducecomputingcomplexityandsignaltonoiseratiothestartingpointistoap proximatethefulldistributionofhighdimensionaldatasetwithasetoflowdimensionalmargi naldistributionsviaoptimizingscorefunctionandreducingsensitivityinwhichgenerationof noisyconditionaldistributionswithdifferentialprivacyiscomputedinasetoflowdimensiona lsubspacesandthenthesampletuplesfromthenoisyapproximationdistributionareusedto generateandreleasethesyntheticdatasetsomecrucialscienceproblemswouldbeinvestiga tedbelowiconstructingalowkdegreebayesiannetworkoverthehighdimensionaldatasetvi aexponentialmechanismindifferentialprivacywherethescorefunctionisoptimizedtoredu cethesensitivityusingmutualinformationequivalenceclassesinmaximumjointdistributio nanddynamicprogrammingiistudyingthealgorithmtocomputeasetofnoisyconditionaldis tributionsfromjointdistributionsinthesubspaceofbayesiannetworkviathelaplacemechan ismofdifferentialprivacyiiiexploringhowtogeneratesyntheticdatafromthedifferentiallypr ivatebayesiannetworkandconditionaldistributionswithoutexplicitlymaterializingthenois yglobaldistributiontheproposedsolutionmayhavetheoreticalandtechnicalsignificancefo rsyntheticdatagenerationwithdifferentialprivacyonbusinessprospects