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IntroductioPnsaisevoSnundsedasAivptoueSnendeSsstcelDeviyrcenConptioionclustpadAérazaM.LhDitywitureceSiv2/1s
Adaptive Security of Symbolic Encryption: the Case of Dynamic Corruptions.
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Laurent Mazaré Joint work with Bogdan Warinschi
LSV - ENS Cachan
Formacrypt Metting, January 2007
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Adversary Model 1Passive adversary: eavesdropper. 2Adaptive adversaries. 3Active adversaries.
Computational soundness of symbolic encryption SymbolicsecurityComputationalsecurity.
Symbolic and Computational Settings 1Symbolicmodel: automatic verification. 2Computationalmodel: realism.
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razaM.LAéadtpviSecerutiywithDynamicCorritpu2sno02/
Symbolic and Computational Settings 1Symbolicmodel: automatic verification. 2Computationalmodel: realism.
Adversary Model 1Passive adversary: eavesdropper. 2Adaptive adversaries. 3Active adversaries.
Computational soundness of symbolic encryption SymbolicsecurityComputationalsecurity.
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Adversary Model 1Passive adversary: eavesdropper. 2Adaptive adversaries. 3Active adversaries.
Computational soundness of symbolic encryption SymbolicsecurityComputationalsecurity.
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Symbolic and Computational Settings 1Symbolicmodel: automatic verification. 2Computationalmodel: realism.
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Passive Soundness
Selective Decryption
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Adaptive Soundness
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