Cryptographic applications of capacity theory: On the optimality of Coppersmith's method for univariate polynomials
May 25, 2016 Β· Declared Dead Β· π International Conference on the Theory and Application of Cryptology and Information Security
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ted Chinburg, Brett Hemenway, Nadia Heninger, Zachary Scherr
arXiv ID
1605.08065
Category
cs.CR: Cryptography & Security
Cross-listed
math.NT
Citations
5
Venue
International Conference on the Theory and Application of Cryptology and Information Security
Last Checked
4 months ago
Abstract
We draw a new connection between Coppersmith's method for finding small solutions to polynomial congruences modulo integers and the capacity theory of adelic subsets of algebraic curves. Coppersmith's method uses lattice basis reduction to construct an auxiliary polynomial that vanishes at the desired solutions. Capacity theory provides a toolkit for proving when polynomials with certain boundedness properties do or do not exist. Using capacity theory, we prove that Coppersmith's bound for univariate polynomials is optimal in the sense that there are \emph{no} auxiliary polynomials of the type he used that would allow finding roots of size $N^{1/d+Ξ΅}$ for monic degree-$d$ polynomials modulo $N$. Our results rule out the existence of polynomials of any degree and do not rely on lattice algorithms, thus eliminating the possibility of even superpolynomial-time improvements to Coppersmith's bound. We extend this result to constructions of auxiliary polynomials using binomial polynomials, and rule out the existence of any auxiliary polynomial of this form that would find solutions of size $N^{1/d+Ξ΅}$ unless $N$ has a very small prime factor.
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