## CryptoDB

### Thijs Laarhoven

#### Publications

**Year**

**Venue**

**Title**

2021

CRYPTO

Lower bounds on lattice sieving and information set decoding
📺
Abstract

In two of the main areas of post-quantum cryptography, based on lattices and codes, nearest neighbor techniques have been used to speed up state-of-the-art cryptanalytic algorithms, and to obtain the lowest asymptotic cost estimates to date [May--Ozerov, Eurocrypt'15; Becker--Ducas--Gama--Laarhoven, SODA'16]. These upper bounds are useful for assessing the security of cryptosystems against known attacks, but to guarantee long-term security one would like to have closely matching lower bounds, showing that improvements on the algorithmic side will not drastically reduce the security in the future. As existing lower bounds from the nearest neighbor literature do not apply to the nearest neighbor problems appearing in this context, one might wonder whether further speedups to these cryptanalytic algorithms can still be found by only improving the nearest neighbor subroutines.
We derive new lower bounds on the costs of solving the nearest neighbor search problems appearing in these cryptanalytic settings. For the Euclidean metric we show that for random data sets on the sphere, the locality-sensitive filtering approach of [Becker--Ducas--Gama--Laarhoven, SODA 2016] using spherical caps is optimal, and hence within a broad class of lattice sieving algorithms covering almost all approaches to date, their asymptotic time complexity of $2^{0.292d + o(d)}$ is optimal. Similar conditional optimality results apply to lattice sieving variants, such as the $2^{0.265d + o(d)}$ complexity for quantum sieving [Laarhoven, PhD thesis 2016] and previously derived complexity estimates for tuple sieving [Herold--Kirshanova--Laarhoven, PKC 2018]. For the Hamming metric we derive new lower bounds for nearest neighbor searching which almost match the best upper bounds from the literature [May--Ozerov, Eurocrypt 2015]. As a consequence we derive conditional lower bounds on decoding attacks, showing that also here one should search for improvements elsewhere to significantly undermine security estimates from the literature.

2020

PKC

The Randomized Slicer for CVPP: Sharper, Faster, Smaller, Batchier
📺
Abstract

Following the recent line of work on solving the closest vector problem with preprocessing (CVPP) using approximate Voronoi cells, we improve upon previous results in the following ways: We derive sharp asymptotic bounds on the success probability of the randomized slicer, by modelling the behaviour of the algorithm as a random walk on the coset of the lattice of the target vector. We thereby solve the open question left by Doulgerakis–Laarhoven–De Weger [PQCrypto 2019] and Laarhoven [MathCrypt 2019]. We obtain better trade-offs for CVPP and its generalisations (strictly, in certain regimes), both with and without nearest neighbour searching, as a direct result of the above sharp bounds on the success probabilities. We show how to reduce the memory requirement of the slicer, and in particular the corresponding nearest neighbour data structures, using ideas similar to those proposed by Becker–Gama–Joux [Cryptology ePrint Archive, 2015]. Using $$2^{0.185d + o(d)}$$ memory, we can solve a single CVPP instance in $$2^{0.264d + o(d)}$$ time. We further improve on the per-instance time complexities in certain memory regimes, when we are given a sufficiently large batch of CVPP problem instances for the same lattice. Using $$2^{0.208d + o(d)}$$ memory, we can heuristically solve CVPP instances in $$2^{0.234d + o(d)}$$ amortized time, for batches of size at least $$2^{0.058d + o(d)}$$ . Our random walk model for analysing arbitrary-step transition probabilities in complex step-wise algorithms may be of independent interest, both for deriving analytic bounds through convexity arguments, and for computing optimal paths numerically with a shortest path algorithm. As a side result we apply the same random walk model to graph-based nearest neighbour searching, where we improve upon results of Laarhoven [SOCG 2018] by deriving sharp bounds on the success probability of the corresponding greedy search procedure.

2018

PKC

Speed-Ups and Time–Memory Trade-Offs for Tuple Lattice Sieving
Abstract

In this work we study speed-ups and time–space trade-offs for solving the shortest vector problem (SVP) on Euclidean lattices based on tuple lattice sieving.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 trade-offs. The trade-offs we obtain stem from the generalization and combination of two algorithmic techniques: the configuration framework introduced by Herold–Kirshanova, and the spherical locality-sensitive filters of Becker–Ducas–Gama–Laarhoven [SODA’16].When the available memory scales quasi-linearly with the list size, we show that with triple sieving we can solve SVP in dimension n in time
$$2^{0.3588n + o(n)}$$
20.3588n+o(n) and space
$$2^{0.1887n + o(n)}$$
20.1887n+o(n), improving upon the previous best triple sieve time complexity of
$$2^{0.3717n + o(n)}$$
20.3717n+o(n) of Herold–Kirshanova. Using more memory we obtain better asymptotic time complexities. For instance, we obtain a triple sieve requiring only
$$2^{0.3300n + o(n)}$$
20.3300n+o(n) time and
$$2^{0.2075n + o(n)}$$
20.2075n+o(n) memory to solve SVP in dimension n. This improves upon the best double Gauss sieve of Becker–Ducas–Gama–Laarhoven, which runs in
$$2^{0.3685n + o(n)}$$
20.3685n+o(n) time when using the same amount of space.

#### Coauthors

- Léo Ducas (1)
- Gottfried Herold (1)
- Elena Kirshanova (2)
- Wessel P. J. van Woerden (1)