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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.
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.
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).
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.