Most homomorphic encryption (FHE) schemes exploit a technique called single-instruction multiple-data (SIMD) to process several messages in parallel. However, they base their security in somehow strong assumptions, such as the hardness of approximate lattice problems with superpolynomial approximation factor. On the other extreme of the spectrum, there are lightweight FHE schemes that have much faster bootstrapping but no SIMD capabilities. On the positive side, the security of these schemes is based on lattice problems with (low-degree) polynomial approximation factor only, which is a much weaker security assumption. Aiming the best of those two options, Micciancio and Sorrell (ICALP'18) proposed a new amortized bootstrapping that can process many messages at once, yielding sublinear time complexity per message, and allowing one to construct FHE based on lattice problems with polynomial approximation factor.

Summary Most of the applications in the seismology field rely on the processing of up to hundreds of terabytes of data and their performance is strongly affected by IO operations. In this article, we analyze the main file structures currently used to store seismic data and propose a new intermediate data structure to improve IO performance while still complying with established standards. We show that, throughout a common workflow in seismic data analysis, our IO performance gain greatly surpasses the overhead of translating data to the intermediate structure. This approach enables a speedup of up to 208 times in reading time when using classical standards (e.g., SEG‐Y) and our intermediate structure is up to 1.8 times more efficient than modern formats (e.g., ASDF). Considering cache‐friendly applications, our speedups over the direct use of SEG‐Y reach 8000 times. We also performed a cost analysis on the AWS cloud showing that, in our approach, HDDs can be 1.25 times more cost‐effective than SSDs.

Summary This paper presents a new enhanced version of the QcBits key encapsulation mechanism, which is a constant‐time implementation of the Niederreiter cryptosystem using QC‐MDPC codes. In this version, we updated the implementation parameters to meet the 128‐bit quantum security level, replaced some of the core algorithms to avoid using slower instructions, vectorized the entire code using the AVX‐512 instruction set extension, and applied several other techniques to achieve a competitive performance level. Our implementation takes 928, 259, and 5008 thousand Skylake cycles to perform batch key generation (cost per key), encryption, and uniform decryption, respectively. Comparing with the current state‐of‐the‐art implementation for QC‐MDPC codes, BIKE, our code is 1.9 times faster when decrypting messages.