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Toward Proxy Re-encryption From Learning with Errors in the Exponent
- Source :
- TrustCom/BigDataSE/ICESS
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- Proxy re-encryption (PRE) is an important cryptographic primitive used for private information sharing. However, the recent advance in quantum computer has potentially crippled its security, as the traditional decisional Diffie-Hellman (DDH)-based PRE is venerable to the quantum attack. Thus, learning with errors (LWE)-based PRE schemes, as a kind of latticebased construction with the inherent quantum-resistant property, has attracted special research interest. Unfortunately, the main drawback of lattice-based public key encryption scheme is noise management after multiplication evaluation. Many cryptographers have been devoted to controlling the expansion of noise. In this line of work, Dagdelen-Gajek-G¨opfert (DGG) put forth the notion of learning with errors in the exponent (LWEE) which is based on lattice and group-theoretic assumption, meanwhile demonstrated a paradigm for constructing efficient quantum resistance public key schemes. In this paper, on top of DGG, we construct a single-bit, single-hop and unidirectional LWEE- based PRE scheme with indistinguishable chosen plaintext attack (IND-CPA) security. To the best of our knowledge, our scheme is the first LWEE-based PRE scheme.
- Subjects :
- Theoretical computer science
Cryptographic primitive
Computer science
business.industry
020207 software engineering
02 engineering and technology
Encryption
Proxy re-encryption
Public-key cryptography
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Chosen-plaintext attack
business
Private information retrieval
Learning with errors
Quantum computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2017 IEEE Trustcom/BigDataSE/ICESS
- Accession number :
- edsair.doi...........888f38d821da78bd9484f66aab46884a
- Full Text :
- https://doi.org/10.1109/trustcom/bigdatase/icess.2017.300