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Explicit Randomness is not Necessary when Modeling Probabilistic Encryption

Authors: Cortier, Véronique; Hoerdegen, Heinrich; Warinschi, Bogdan;

Explicit Randomness is not Necessary when Modeling Probabilistic Encryption

Abstract

Although good encryption functions are probabilistic, most symbolic models do not capture this aspect explicitly. A typical solution, recently used to prove the soundness of such models with respect to computational ones, is to explicitly represent the dependency of ciphertexts on random coins as labels. In order to make these label-based models useful, it seems natural to try to extend the underlying decision procedures and the implementation of existing tools. In this paper we put forth a more practical alternative based on the following soundness theorem. We prove that for a large class of security properties (that includes rather standard formulations for secrecy and authenticity properties), security of protocols in the simpler model implies security in the label-based model. Combined with the soundness result of (\textbf{?}) our theorem enables the translation of security results in unlabeled symbolic models to computational security.

Countries
United Kingdom, France, France
Keywords

FOS: Computer and information sciences, probabilistic encryption, Computer Science - Cryptography and Security, Probabilistic, 510, 004, Theoretical Computer Science, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], secrecy, protocol verification, security models, authentication, Cryptography and Security (cs.CR), encryption, [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR], Computer Science(all)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Average
Green
gold