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The Shortest Vector Problem (SVP) is one of the mathematical foundations of lattice based cryptography. Lattice sieve algorithms are amongst the foremost methods of solving SVP. The asymptotically fas...
In this work we consider a progressive approach to lattice sieving, where we gradually introduce new basis vectors only when the sieve has stabilized on the previous basis vectors. This leads to impro...
Our results extend and improve upon previous work of Bai-Laarhoven-Stehlé [ANTS'16] and Herold-Kirshanova [PKC'17], with better complexities for arbitrary tuple sizes and offering tunable time-memory ...
Asymptotically, the best known algorithms for solving the Shortest Vector Problem (SVP) in a lattice of dimension nn are sieve algorithms, which have heuristic complexity estimates ranging from (4/3)n...
In practice, the large memory footprint makes it problematic to run sieving directly on high-dimensional lattices, and perhaps the most promising application of such algorithms is as part of a hybrid ...
Lattice sieving is asymptotically the fastest approach for solving the shortest vector problem (SVP) on Euclidean lattices. All known sieving algorithms for solving SVP require space which (heuristica...
We give a simple heuristic sieving algorithm for the m-dimensional exact shortest vector problem (SVP) which runs in time 20.3112m+o(m) . Unlike previous time-memory trade-offs, we do not increase ...
Combining the efficient cross-polytope locality-sensitive hash family of Terasawa and Tanaka with the heuristic lattice sieve algorithm of Micciancio and Voulgaris, we show how to obtain heuristic and...
To solve the approximate nearest neighbor search problem (NNS) on the sphere, we propose a method using locality-sensitive filters (LSF), with the property that nearby vectors have a higher probabi...

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