## CryptoDB

### Elena Kirshanova

#### Publications

Year
Venue
Title
2021
CRYPTO
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.
2019
EUROCRYPT
We propose the General Sieve Kernel (G6K, pronounced / e.si.ka/), an abstract stateful machine supporting a wide variety of lattice reduction strategies based on sieving algorithms. Using the basic instruction set of this abstract stateful machine, we first give concise formulations of previous sieving strategies from the literature and then propose new ones. We then also give a light variant of BKZ exploiting the features of our abstract stateful machine. This encapsulates several recent suggestions (Ducas at Eurocrypt 2018; Laarhoven and Mariano at PQCrypto 2018) to move beyond treating sieving as a blackbox SVP oracle and to utilise strong lattice reduction as preprocessing for sieving. Furthermore, we propose new tricks to minimise the sieving computation required for a given reduction quality with mechanisms such as recycling vectors between sieves, on-the-fly lifting and flexible insertions akin to Deep LLL and recent variants of Random Sampling Reduction.Moreover, we provide a highly optimised, multi-threaded and tweakable implementation of this machine which we make open-source. We then illustrate the performance of this implementation of our sieving strategies by applying G6K to various lattice challenges. In particular, our approach allows us to solve previously unsolved instances of the Darmstadt SVP (151, 153, 155) and LWE (e.g. (75, 0.005)) challenges. Our solution for the SVP-151 challenge was found 400 times faster than the time reported for the SVP-150 challenge, the previous record. For exact-SVP, we observe a performance crossover between G6K and FPLLL’s state of the art implementation of enumeration at dimension 70.
2019
ASIACRYPT
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 fastest known classical and quantum sieves solve SVP in a d-dimensional lattice in $2^{\mathsf {c}d + o(d)}$ time steps with $2^{\mathsf {c}' d + o(d)}$ memory for constants $c, c'$ . In this work, we give various quantum sieving algorithms that trade computational steps for memory.We first give a quantum analogue of the classical k-Sieve algorithm [Herold–Kirshanova–Laarhoven, PKC’18] in the Quantum Random Access Memory (QRAM) model, achieving an algorithm that heuristically solves SVP in $2^{0.2989d + o(d)}$ time steps using $2^{0.1395d + o(d)}$ memory. This should be compared to the state-of-the-art algorithm [Laarhoven, Ph.D Thesis, 2015] which, in the same model, solves SVP in $2^{0.2653d + o(d)}$ time steps and memory. In the QRAM model these algorithms can be implemented using $\mathrm {poly}(d)$ width quantum circuits.Secondly, we frame the k-Sieve as the problem of k-clique listing in a graph and apply quantum k-clique finding techniques to the k-Sieve.Finally, we explore the large quantum memory regime by adapting parallel quantum search [Beals et al., Proc. Roy. Soc. A’13] to the 2-Sieve, and give an analysis in the quantum circuit model. We show how to solve SVP in $2^{0.1037d + o(d)}$ time steps using $2^{0.2075d + o(d)}$ quantum memory.
2018
PKC
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.
2018
PKC
The hardness of the learning with errors (LWE) problem is one of the most fruitful resources of modern cryptography. In particular, it is one of the most prominent candidates for secure post-quantum cryptography. Understanding its quantum complexity is therefore an important goal.We show that under quantum polynomial time reductions, LWE is equivalent to a relaxed version of the dihedral coset problem (DCP), which we call extrapolated DCP (eDCP). The extent of extrapolation varies with the LWE noise rate. By considering different extents of extrapolation, our result generalizes Regev’s famous proof that if DCP is in BQP (quantum poly-time) then so is LWE (FOCS 02). We also discuss a connection between eDCP and Childs and Van Dam’s algorithm for generalized hidden shift problems (SODA 07).Our result implies that a BQP solution for LWE might not require the full power of solving DCP, but rather only a solution for its relaxed version, eDCP, which could be easier.
2017
PKC
2014
PKC

Crypto 2020
Asiacrypt 2019