Anonymous HIBE with Short Ciphertexts: Full Security in Prime Order Groups
February 27, 2015 Β· Declared Dead Β· π Designs, Codes and Cryptography
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kwangsu Lee, Jong Hwan Park, Dong Hoon Lee
arXiv ID
1502.07812
Category
cs.CR: Cryptography & Security
Citations
22
Venue
Designs, Codes and Cryptography
Last Checked
3 months ago
Abstract
Anonymous Hierarchical Identity-Based Encryption (HIBE) is an extension of Identity-Based Encryption (IBE), and it provides not only a message hiding property but also an identity hiding property. Anonymous HIBE schemes can be applicable to anonymous communication systems and public key encryption systems with keyword searching. However, previous anonymous HIBE schemes have some disadvantages that the security was proven in the weaker model, the size of ciphertexts is not short, or the construction was based on composite order bilinear groups. In this paper, we propose the first efficient anonymous HIBE scheme with short ciphertexts in prime order (asymmetric) bilinear groups, and prove its security in the full model with an efficient reduction. To achieve this, we use the dual system encryption methodology of Waters. We also present the benchmark results of our scheme by measuring the performance of our implementation.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted