A Unified Cryptoprocessor for Lattice-based Signature and Key-exchange
October 13, 2022 Β· Declared Dead Β· π IEEE transactions on computers
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Aikata Aikata, Ahmet Can Mert, David Jacquemin, Amitabh Das, Donald Matthews, Santosh Ghosh, Sujoy Sinha Roy
arXiv ID
2210.07412
Category
cs.CR: Cryptography & Security
Cross-listed
cs.AR
Citations
37
Venue
IEEE transactions on computers
Last Checked
6 months ago
Abstract
We propose design methodologies for building a compact, unified and programmable cryptoprocessor architecture that computes post-quantum key agreement and digital signature. Synergies in the two types of cryptographic primitives are used to make the cryptoprocessor compact. As a case study, the cryptoprocessor architecture has been optimized targeting the signature scheme 'CRYSTALS-Dilithium' and the key encapsulation mechanism (KEM) 'Saber', both finalists in the NIST's post-quantum cryptography standardization project. The programmable cryptoprocessor executes key generations, encapsulations, decapsulations, signature generations, and signature verifications for all the security levels of Dilithium and Saber. On a Xilinx Ultrascale+ FPGA, the proposed cryptoprocessor consumes 18,406 LUTs, 9,323 FFs, 4 DSPs, and 24 BRAMs. It achieves 200 MHz clock frequency and finishes CCA-secure key-generation/encapsulation/decapsulation operations for LightSaber in 29.6/40.4/58.3$ΞΌ$s; for Saber in 54.9/69.7/94.9$ΞΌ$s; and for FireSaber in 87.6/108.0/139.4$ΞΌ$s, respectively. It finishes key-generation/sign/verify operations for Dilithium-2 in 70.9/151.6/75.2$ΞΌ$s; for Dilithium-3 in 114.7/237/127.6$ΞΌ$s; and for Dilithium-5 in 194.2/342.1/228.9$ΞΌ$s, respectively, for the best-case scenario. On UMC 65nm library for ASIC the latency is improved by a factor of two due to a 2x increase in clock frequency.
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