Universal Construction of Cheater-Identifiable Secret Sharing Against Rushing Cheaters Based on Message Authentication
January 16, 2017 Β· Declared Dead Β· π IACR Cryptology ePrint Archive
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Masahito Hayashi, Takeshi Koshiba
arXiv ID
1701.04470
Category
cs.CR: Cryptography & Security
Cross-listed
cs.IT
Citations
6
Venue
IACR Cryptology ePrint Archive
Last Checked
4 months ago
Abstract
For conventional secret sharing, if cheaters can submit possibly forged shares after observing shares of the honest users in the reconstruction phase then they cannot only disturb the protocol but also only they may reconstruct the true secret. To overcome the problem, secret sharing scheme with properties of cheater-identification have been proposed. Existing protocols for cheater-identifiable secret sharing assumed non-rushing cheaters or honest majority. In this paper, we remove both conditions simultaneously, and give its universal construction from any secret sharing scheme. To resolve this end, we propose the concepts of "individual identification" and "agreed identification".
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