Parallel Computation of Optimal Ate Cryptographic Pairings at the $128$, $192$ and $256$-bit security levels using elliptic net algorithm
March 25, 2020 Β· Declared Dead Β· + Add venue
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Narcisse Bang Mbang, Emmanuel Fouotsa, Celestin Lele
arXiv ID
2003.11286
Category
math.AG
Cross-listed
cs.CR,
math.NT
Citations
0
Last Checked
3 months ago
Abstract
Efficient computations of pairings with Miller Algorithm have recently received a great attention due to the many applications in cryptography. In this work, we give formulae for the optimal Ate pairing in terms of elliptic nets associated to twisted Barreto-Naehrig (BN) curve, Barreto-Lynn-Scott(BLS) curves and Kachisa-Schaefer-Scott(KSS) curves considered at the $128$, $192$ and $256$-bit security levels, and Scott-Guillevic curve with embedding degree $54$. We show how to parallelize the computation of these pairings when the elliptic net algorithm instead is used and we obtain except in the case of Kachisa-Schaefer-Scott(KSS) curves considered at the $256$-bit security level, more efficient theoretical results with $8$ processors compared to the case where the Miller algorithm is used. This work still confirms that $BLS48$ curves are the best for pairing-based cryptography at $256$-bit security level \cite{NARDIEFO19}.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β math.AG
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Two-point AG codes on the GK maximal curves
R.I.P.
π»
Ghosted
Congruences and Concurrent Lines in Multi-View Geometry
R.I.P.
π»
Ghosted
Quantum codes from a new construction of self-orthogonal algebraic geometry codes
R.I.P.
π»
Ghosted
The Chow Form of the Essential Variety in Computer Vision
R.I.P.
π»
Ghosted
Algebraic Geometric codes from Kummer Extensions
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