Public-Key Encryption Indistinguishable Under Plaintext-Checkable Attacks - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

Public-Key Encryption Indistinguishable Under Plaintext-Checkable Attacks

Résumé

Indistinguishability under adaptive chosen-ciphertext attack (IND-CCA) is now considered the de facto security notion for public-key encryption. However, the security guarantee that it offers is sometimes stronger than what is needed by certain applications. In this paper, we consider a weaker notion of security for public-key encryption, termed indistinguishability under plaintext-checking attacks (IND-PCA), in which the adversary is only given access to an oracle which says whether or not a given ciphertext encrypts a given message. After formaliz- ing the IND-PCA notion, we then design a new public-key encryption scheme satisfying it. The new scheme is a more efficient variant of the Cramer-Shoup encryption scheme with shorter ciphertexts and its secu- rity is also based on the plain Decisional Diffie-Hellman (DDH) assump- tion. Additionally, the algebraic properties of the new scheme also allow for proving plaintext knowledge using Groth-Sahai non-interactive zero- knowledge proofs or smooth projective hash functions. Finally, in order to illustrate the usefulness of the new scheme, we further show that, for many password-based authenticated key exchange (PAKE) schemes in the Bellare-Pointcheval-Rogaway security model, one can safely replace the underlying IND-CCA encryption schemes with our new IND-PCA one. By doing so, we were able to reduce the overall communication complex- ity of these protocols and obtain the most efficient PAKE schemes to date based on the plain DDH assumption.
Fichier non déposé

Dates et versions

hal-01131982 , version 1 (16-03-2015)

Identifiants

Citer

Michel Abdalla, Fabrice Benhamouda, David Pointcheval. Public-Key Encryption Indistinguishable Under Plaintext-Checkable Attacks. PKC 2015, Mar 2015, Maryland, United States. pp.332-352, ⟨10.1007/978-3-662-46447-2_15⟩. ⟨hal-01131982⟩
3530 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More