Virginia加密算法举例 ml m2 m3 m4 m5 m6 m7 m8 m9 ml0 mll 明文M n 0 t h n g i S 0 (编码) (13) (14) (19) (7) (8) (13) (8) (18) (19) (14) 秘钥K j y 0 y y 0 (编码) (9) (14) (24) (9) (14) (24) (9 (14) (24) (9 (14) 密文C r W p W c (编码) (22) (2) (17) (16) (22) (11) (15) (22) (16) (2) (2) j=1j=2j=3 j=1j=2j=3 j=1j=2j=3 j护1 j=2 t=0t=0 =0t=1t=1t=1t=2t=2t=2 t=3 t=3 明文长度n=11,秘钥长度d=3, ceiling(11/3)-1=3
Virginia加密算法举例 m1 m2 m3 m4 m5 m6 m7 m8 m9 m10 m11 明文M (编码) n (13) o (14) t (19) h (7) i (8) n (13) g (6) i (8) s (18) t (19) o (14) 秘钥K (编码) j (9) o (14) y (24) j (9) o (14) y (24) j (9) o (14) y (24) j (9) o (14) 密文C (编码) w (22) c (2) r (17) q (16) w (22) l (11) p (15) w (22) q (16) c (2) c (2) j=1 t=0 j=2 t=0 j=3 t=0 j=1 t=1 j=2 t=1 j=3 t=1 j=1 t=2 j=2 t=2 j=3 t=2 j=1 t=3 j=2 t=3 明文长度n=11，秘钥长度d=3， t=ceiling(11/3)-1=3
一个原始的明文文本 Differential Privacy is the state-of-the-art goal for the problem of privacy-preserving data release and privacy-preserving data mining.Existing techniques using differential privacy,however, cannot effectively handle the publication of high-dimensional data.In particular,when the input dataset contains a large number of attributes,existing methods incur higher computing complexity and lower information to noise ratio,which renders the published data next to useless. This proposal aims to reduce computing complexity and signal to noise ratio.The starting point is to approximate the full distribution of high-dimensional dataset with a set of low-dimensional marginal distributions via optimizing score function and reducing sensitivity,in which generation of noisy conditional distributions with differential privacy is computed in a set of low-dimensional subspaces,and then,the sample tuples from the noisy approximation distribution are used to generate and release the synthetic dataset.Some crucial science problems would be investigated below:(i)constructing a low k-degree Bayesian network over the high-dimensional dataset via exponential mechanism in differential privacy,where the score function is optimized to reduce the sensitivity using mutual information,equivalence classes in maximum joint distribution and dynamic programming;(ii)studying the algorithm to compute a set of noisy conditional distributions from joint distributions in the subspace of Bayesian network,via the Laplace mechanism of differential privacy.(iii)exploring how to generate synthetic data from the differentially private Bayesian network and conditional distributions,without explicitly materializing the noisy global distribution.The proposed solution may have theoretical and technical significance for synthetic data generation with differential privacy on business prospects
一个原始的明文文本 Differential Privacy is the state-of-the-art goal for the problem of privacy-preserving data release and privacy-preserving data mining. Existing techniques using differential privacy, however, cannot effectively handle the publication of high-dimensional data. In particular, when the input dataset contains a large number of attributes, existing methods incur higher computing complexity and lower information to noise ratio, which renders the published data next to useless. This proposal aims to reduce computing complexity and signal to noise ratio. The starting point is to approximate the full distribution of high-dimensional dataset with a set of low-dimensional marginal distributions via optimizing score function and reducing sensitivity, in which generation of noisy conditional distributions with differential privacy is computed in a set of low-dimensional subspaces, and then, the sample tuples from the noisy approximation distribution are used to generate and release the synthetic dataset. Some crucial science problems would be investigated below: (i) constructing a low k-degree Bayesian network over the high-dimensional dataset via exponential mechanism in differential privacy, where the score function is optimized to reduce the sensitivity using mutual information, equivalence classes in maximum joint distribution and dynamic programming; (ii)studying the algorithm to compute a set of noisy conditional distributions from joint distributions in the subspace of Bayesian network, via the Laplace mechanism of differential privacy. (iii)exploring how to generate synthetic data from the differentially private Bayesian network and conditional distributions, without explicitly materializing the noisy global distribution. The proposed solution may have theoretical and technical significance for synthetic data generation with differential privacy on business prospects
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
经过virginia加密后的密文 加密秘钥key=infosec lvktwvgvgnodttqifqqmubujglevmbkhziczglcsphweyvwttwoqseshxenjsgaxejgwvxqalrsxczrqsswgiaid jmxipddjiumeawfkfigfaarkvtjlawvqalhwgjvwiwwwavsuvmhnrwsfxkiyufazckImcoixmehofrqbrktwg vqijzqlcvqqsllgxhgzagcbvtbgjjqtmraqgvfncfenInyoarriey wuyniebvwrvprnbhyvInyokivkbshsmpanqo jkgvhrpwvqnnyhjmdcgjgwbkagnbyqgbutrkmpkhwvakjmehcetwbvsuusoxyjlaxaiaizgagzvstgvoigncf xqvbngwvcbvtkzmepejbvitagmshydtvxvwhfigfbwbvbbzgwpgafyvawrzbuckenivrglstmqzqwgquczha rikbrddizqgdofhuqtsodxqvbngwvcbvthziubnwharixbnblmubbfdhvqfvrolivprkidpfqfyfafwbvtbgjjqt mraqgvfncfenInyokivevyvswgbbkzgafqzjbkmqvnqasviqafzvmubenpmxkwaxjaeqxgnaadkvtxqgvgnh sqlmqvnsrjifcpnbywgvfnhazkbInbolkkulsfitigncfshvbngqgqvqnhaspiyiwkxtqozhaspajnhzhknsjfwrv qnqdjmxipdwkgquczhwhkvnxslshtbbraqgvfncfenahggheemffbvxjmayvwwcucqslyrtrxtjsobujbgmu gnudjszqzfhasplvxhjmdcgncfetmhxsvxqorssjevmnsrjinmnxsllgalshzivqpioleumgxceiezhhwspukvjbu irzbgzwquebzzvfgqaaskxkonysvfgtbbwuspagwiuxkvtfzgamIrlfwidiljgaepvrykgvmwijfllgpvlwvmomax wgrctqfhswgbinowbrwajblmctzjqzepqfrwfhknsjfwrvqnqdjmxipdkzitmgmskgqzrkifgvqbswksrbvrwr ifbbwsvyemgmskipavywnmvghxwfkocgzodmpnbwasxkwajemmxiyjbuietnxgwwkvzflaqwuwtwfxfqf yfafwbvtbsrfllsoemexetujeouvsuamubhimaribujodkqzvyvexqkbrdmxgifjhgjpwvxmusplvywgrctqng lvkjhywgrunetabskvgiwkxtqozhaspavshziucoxdsggwsgoqiuqnsbwxywepjaevprqohpckrrsulcvvxagjf qsksjipbvfzhvkdnhmamkmkuzgvkvtmcoxqorssjevmfdbllgbvhrsxcnetallglvktwvgvgnodpaxenjsxgjnd skmcvajhostsnsrusplvywgrctqnglvkjhywgruevyvgyvmkuzagkbydasxgzvfzadkvtyvwrqqfdudsdiyiwkx tqozhaspbujdjsrwfjrksncgncfqcgufjwxjmbwslmeiyfbvxgkuswuenavlbajkknsqwjqzfdbllgbvhrsxcorss jevqbskaxjlvktwvgvgnodttqifqqspjhxwfiuacwcktgkgxvzlj
经过virginia加密后的密文 加密秘钥key=infosec lvktwvgvgnodttqifqqmubujglevmbkhziczglcsphweyvwttwoqseshxenjsgaxejgwvxqalrsxczrqsswgiaid jmxipddjiumeawfkfigfaarkvtjlawvqalhwgjvvviwwwavsuvmhnrwsfxkiyufazcklmcoixmehofrqbrktwg vqijzqlcvqqsllgxhgzagcbvtbgjjqtmraqgvfncfenlnyoarrieywuyniebvwrvprnbhyvlnyokivkbshsmpanqo jkgvhrpwvqnnyhjmdcgjgwbkagnbyqgbutrkmpkhwvakjmehcetwbvsuusoxyjlaxaiaizgagzvstgvoigncf xqvbngwvcbvtkzmepejbvitagmshydtvxvwhfigfbwbvbbzgwpgafyvawrzbuckenivrglstmqzqwgquczha rikbrddizqgdofhuqtsodxqvbngwvcbvthziubnwharixbnblmubbfdhvqfvrolivprkidpfqfyfafwbvtbgjjqt mraqgvfncfenlnyokivevyvswgbbkzgafqzjbkmqvnqasviqafzvmubenpmxkwaxjaeqxgnaadkvtxqgvgnh sqlmqvnsrjifcpnbywgvfnhazkblnbolkkulsfitigncfshvbngqgqvqnhaspiyiwkxtqozhaspajnhzhknsjfwrv qnqdjmxipdwkgquczhwhkvnxslshtbbraqgvfncfenahggheemffbvxjmayvwwcucqslyrtrxtjsobujbgmu gnudjszqzfhasplvxhjmdcgncfetmhxsvxqorssjevmnsrjinmnxsllgalshzivqpioleumgxceiezhhwspukvjbu irzbgzwquebzzvfgqaaskxkonysvfgtbbwuspagwiuxkvtfzgamlrlfwidiljgaepvrykgvmwijfllgpvlvvmomax wgrctqfhswgbinowbrwajblmctzjqzepqfrwfhknsjfwrvqnqdjmxipdkzitmgmskgqzrkifgvqbswksrbvrwr ifbbwsvyemgmskipavywnmvghxwfkocgzodmpnbwasxkwajemmxiyjbuietnxgwwkvzflaqwuwtwfxfqf yfafwbvtbsrfllsoemexetujeouvsuamubhimaribujodkqzvyvexqkbrdmxgifjhgjpwvxmusplvywgrctqng lvkjhywgrunetabskvgiwkxtqozhaspavshziucoxdsggwsgoqiuqnsbwxywepjaevprqohpckrrsulcvvxagjf qsksjipbvfzhvkdnhmamkmkuzgvkvtmcoxqorssjevmfdbllgbvhrsxcnetallglvktwvgvgnodpaxenjsxgjnd skmcvajhostsnsrusplvywgrctqnglvkjhywgruevyvgyvmkuzagkbydasxgzvfzadkvtyvwrqqfdudsdiyiwkx tqozhaspbujdjsrwfjrksncgncfqcgufjwxjmbwslmeiyfbvxgkuswuenavlbajkknsqwjqzfdbllgbvhrsxcorss jevqbskaxjlvktwvgvgnodttqifqqspjhxwfiuacwcktgkgxvzlj