Authors:
Sana Belguith
1
;
Nesrine Kaaniche
2
;
Maryline Laurent
2
;
Abderrazak Jemai
3
and
Rabah Attia
1
Affiliations:
1
Laboratory of Electronic Systems and Communication Network and Tunisia Polytechnic School, Tunisia
;
2
SAMOVAR, CNRS, Telecom SudParis and University Paris-Saclay, France
;
3
Laboratory LIP2 and University of Sciences of Tunis, Tunisia
Keyword(s):
Attribute based Signcryption, Public Clouds, Privacy, Confidentiality, Access Control, Anonymous Data Origin Authentication.
Related
Ontology
Subjects/Areas/Topics:
Access Control
;
Applied Cryptography
;
Cryptographic Techniques and Key Management
;
Data and Application Security and Privacy
;
Data Engineering
;
Data Protection
;
Databases and Data Security
;
Information and Systems Security
;
Internet Technology
;
Security and Privacy in the Cloud
;
Web Information Systems and Technologies
Abstract:
In this paper, we propose a novel constant-size threshold attribute-based signcryption scheme for securely sharing data through public clouds. Our proposal has several advantages. First, it provides flexible cryptographic access control, while preserving users' privacy as the identifying information for satisfying the access control policy are not revealed. Second, the proposed scheme guarantees both data origin authentication and anonymity thanks to the novel use of attribute based signcryption mechanism, while ensuring the unlinkability between the different access sessions.
Third, the proposed signcryption scheme has efficient computation cost and constant communication overhead whatever the number of involved attributes. Finally, our scheme satisfies strong security properties in the random oracle model, namely Indistinguishability against the Adaptive Chosen Ciphertext Attacks (IND-CCA2), Existential Unforgeability against Chosen Message Attacks (EUF-CMA) and privacy preservat
ion of the attributes involved in the signcryption process, based on the assumption that the augmented Multi-Sequence of Exponents Decisional Diffie-Hellman (aMSE-DDH) problem and the Computational Diffie Hellman Assumption (CDH) are hard.
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