Augmented Learning with Errors: The Untapped Potential of the Error Term, by Rachid El Bansarkhani and Özgür Dagdelen and Johannes Buchmann
The Learning with Errors (LWE) problem has gained a lot of attention in recent years leading to a series of new cryptographic applications. Specifically, it states that it is hard to distinguish random linear equations disguised by some small error from truly random ones. Interestingly, cryptographic primitives based on LWE often do not exploit the full potential of the error term beside of its importance for security.
To this end, we introduce a novel LWE-close assumption, namely Augmented Learning with Errors (A-LWE), which allows to hide auxiliary data injected into the error term by a technique that we call message embedding. In particular, it enables existing cryptosystems to strongly increase the message throughput per ciphertext. We show that A-LWE is for certain instantiations at least as hard as the LWE problem. This inherently leads to new cryptographic constructions providing high data load encryption and customized security properties as required, for instance, in economic environments such as stock markets resp. for financial transactions. The security of those constructions basically stems from the hardness to solve the A-LWE problem.
As an application we introduce (among others) the first lattice-based replayable chosen-ciphertext secure encryption scheme from A-LWE.
S-box pipelining using genetic algorithms for high-throughput AES implementations: How fast can we go?, by Lejla Batina and Domagoj Jakobovic and Nele Mentens and Stjepan Picek and Antonio de la Piedr
In the last few years, several practitioners have proposed a
wide range of approaches for reducing the implementation area of the
AES in hardware. However, an area-throughput trade-off that undermines high-speed is not realistic for real-time cryptographic applications. In this manuscript, we explore how Genetic Algorithms (GAs) can be used for pipelining the AES substitution box based on composite field arithmetic. We implemented a framework that parses and analyzes a Verilog netlist, abstracts it as a graph of interconnected cells and generates circuit statistics on its elements and paths. With this information, the GA extracts the appropriate arrangement of Flip-Flops (FFs) that maximizes the throughput of the given netlist. In doing so, we show that it is possible to achieve a 50 % improvement in throughput with only an 18 % increase in area in the UMC 0.13 um low-leakage standard cell library.
Cube Attacks and Cube-attack-like Cryptanalysis on the Round-reduced Keccak Sponge Function, by Itai Dinur and Pawel Morawiecki and Josef Pieprzyk and Marian Srebrny and Michal Straus
In this paper, we comprehensively study the resistance of keyed variants of SHA-3 (Keccak) against algebraic attacks. This analysis covers a wide range of key recovery, MAC forgery and other types of attacks, breaking up to 9 rounds (out of the full 24) of the Keccak internal permutation much faster than exhaustive search. Moreover, some of our attacks on the 6-round Keccak are completely practical and were verified on a desktop PC. Our methods combine cube attacks (an algebraic key recovery attack) and related algebraic techniques with structural analysis of the Keccak permutation. These techniques should be useful in future cryptanalysis of Keccak and similar designs.
Although our attacks break more rounds than previously published techniques, the security margin of Keccak remains large. For Keyak -- a Keccak-based authenticated encryption scheme -- the nominal number of rounds is 12 and therefore its security margin is smaller (although still sufficient).
A comprehensive empirical comparison of parallel ListSieve and GaussSieve, by Artur Mariano and Ozgur Dagdelen and Christian Bischof
The security of lattice-based cryptosystems is determined by
the performance of practical implementations of, among others, algo-
rithms for the Shortest Vector Problem (SVP).
In this paper, we conduct a comprehensive, empirical comparison of two
SVP-solvers: ListSieve and GaussSieve. We also propose a practical par-
allel implementation of ListSieve, which achieves super-linear speedups
on multi-core CPUs, with efficiency levels as high as 183%. By compar-
ing our implementation with a parallel implementation of GaussSieve, we
show that ListSieve can, in fact, outperform GaussSieve for a large num-
ber of threads, thus answering a question that was still open to this day.