Part2:Virginia加密 多表密码是利用多个单表代替密码构成的密码体制。 它在对明文进行加密的过程中依照密钥的指示轮流 便用多个单表代替密码。 明文M=(mvm2,mn,密钥K=(kk2…,ka),密文 C=(C1C2..Cn) 加密变换：C+ta=Ekim+td=m+ta+k mod n 解密变换：m+ta=Dkc+td=C+td-ki mod n e a a Z C V T W Q NG R Z G V WA G 密钥空间为26d
Part2 : Virginia加密 • 多表密码是利用多个单表代替密码构成的密码体制。 它在对明文进行加密的过程中依照密钥的指示轮流 使用多个单表代替密码。 • 明文M=(m1 ,m2 ,…,mn )，密钥 K=(k1 ,k2 ,…,kd ) ，密文 C=(c1 ,c2 ,…,cn ) • 加密变换：ci+td=Eki(mi+td)=mi+td+ki mod n • 解密变换： mi+td=Dki(ci+td)=ci+td - ki mod n 密钥空间为26d
Example 3:plaintext.txt differentialprivacyisthestateoftheartgoalfortheproblemofprivacypreservingdatareleaseandprivacypr eservingdataminingexistingtechniquesusingdifferentialprivacyhowevercannoteffectivelyhandlethep ublicationofhighdimensionaldatainparticularwhentheinputdatasetcontainsalargenumberofattribute sexistingmethodsincurhighercomputingcomplexityandlowerinformationtonoiseratiowhichrendersth epublisheddatanexttouselessthisproposalaimstoreducecomputingcomplexityandsignaltonoiseratiot hestartingpointistoapproximatethefulldistributionofhighdimensionaldatasetwithasetoflowdimensio nalmarginaldistributionsviaoptimizingscorefunctionandreducingsensitivityinwhichgenerationofnois yconditionaldistributionswithdifferentialprivacyiscomputedinasetoflowdimensionalsubspacesandth enthesampletuplesfromthenoisyapproximationdistributionareusedtogenerateandreleasethesynthet icdatasetsomecrucialscienceproblemswouldbeinvestigatedbelowiconstructingalowkdegreebayesian networkoverthehighdimensionaldatasetviaexponentialmechanismindifferentialprivacywherethesco refunctionisoptimizedtoreducethesensitivityusingmutualinformationequivalenceclassesinmaximum jointdistributionanddynamicprogrammingiistudyingthealgorithmtocomputeasetofnoisyconditionald istributionsfromjointdistributionsinthesubspaceofbayesiannetworkviathelaplacemechanismofdiffer entialprivacyiiiexploringhowtogeneratesyntheticdatafromthedifferentiallyprivatebayesiannetworka ndconditionaldistributionswithoutexplicitlymaterializingthenoisyglobaldistributiontheproposedsolu tionmayhavetheoreticalandtechnicalsignificanceforsyntheticdatagenerationwithdifferentialprivacyo nbusinessprospects 利用Virginia加密，key=infosec
Example 3: plaintext.txt • differentialprivacyisthestateoftheartgoalfortheproblemofprivacypreservingdatareleaseandprivacypr eservingdataminingexistingtechniquesusingdifferentialprivacyhowevercannoteffectivelyhandlethep ublicationofhighdimensionaldatainparticularwhentheinputdatasetcontainsalargenumberofattribute sexistingmethodsincurhighercomputingcomplexityandlowerinformationtonoiseratiowhichrendersth epublisheddatanexttouselessthisproposalaimstoreducecomputingcomplexityandsignaltonoiseratiot hestartingpointistoapproximatethefulldistributionofhighdimensionaldatasetwithasetoflowdimensio nalmarginaldistributionsviaoptimizingscorefunctionandreducingsensitivityinwhichgenerationofnois yconditionaldistributionswithdifferentialprivacyiscomputedinasetoflowdimensionalsubspacesandth enthesampletuplesfromthenoisyapproximationdistributionareusedtogenerateandreleasethesynthet icdatasetsomecrucialscienceproblemswouldbeinvestigatedbelowiconstructingalowkdegreebayesian networkoverthehighdimensionaldatasetviaexponentialmechanismindifferentialprivacywherethesco refunctionisoptimizedtoreducethesensitivityusingmutualinformationequivalenceclassesinmaximum jointdistributionanddynamicprogrammingiistudyingthealgorithmtocomputeasetofnoisyconditionald istributionsfromjointdistributionsinthesubspaceofbayesiannetworkviathelaplacemechanismofdiffer entialprivacyiiiexploringhowtogeneratesyntheticdatafromthedifferentiallyprivatebayesiannetworka ndconditionaldistributionswithoutexplicitlymaterializingthenoisyglobaldistributiontheproposedsolu tionmayhavetheoreticalandtechnicalsignificanceforsyntheticdatagenerationwithdifferentialprivacyo nbusinessprospects 利用Virginia加密, key=infosec