## CryptoDB

### Alexander May

#### Publications

Year
Venue
Title
2021
CRYPTO
The LWE problem with its ring variants is today the most prominent candidate for building efficient public key cryptosystems resistant to quantum computers. NTRU-type cryptosystems use an LWE-type variant with small max-norm secrets, usually with ternary coefficients from the set $\{-1,0,1\}$. The presumably best attack on these schemes is a hybrid attack that combines lattice reduction techniques with Odlyzko's Meet-in-the-Middle approach. Odlyzko's algorithm is a classical combinatorial attack that for key space size $\S$ runs in time $\S^{0.5}$. We substantially improve on this Meet-in-the-Middle approach, using the representation technique developed for subset sum algorithms. Asymptotically, our heuristic Meet-in-the-Middle attack runs in time roughly $\S^{0.25}$, which also beats the $\S^{\frac 1 3}$ complexity of the best known quantum algorithm. For the round-3 NIST post-quantum encryptions NTRU and NTRU Prime we obtain non-asymptotic instantiations of our attack with complexity roughly $\S^{0.3}$. As opposed to other combinatorial attacks, our attack benefits from larger LWE field sizes $q$, as they are often used in modern lattice-based signatures. For example, for BLISS and GLP signatures we obtain non-asymptotic combinatorial attacks around $\S^{0.28}$. Our attacks do not invalidate the security claims of the aforementioned schemes. However, they establish improved combinatorial upper bounds for their security. We leave it is an open question whether our new Meet-in-the-Middle attack in combination with lattice reduction can be used to speed up the hybrid attack.
2021
ASIACRYPT
Let $(N,e)$ be an RSA public key, where $N=pq$ is the product of equal bitsize primes $p,q$. Let $d_p, d_q$ be the corresponding secret CRT-RSA exponents. Using a Coppersmith-type attack, Takayasu, Lu and Peng (TLP) recently showed that one obtains the factorization of $N$ in polynomial time, provided that $d_p, d_q \leq N^{0.122}$. Building on the TLP attack, we show the first {\em Partial Key Exposure} attack on short secret exponent CRT-RSA. Namely, let $N^{0.122} \leq d_p, d_q \leq N^{0.5}$. Then we show that a constant known fraction of the least significant bits (LSBs) of both $d_p, d_q$ suffices to factor $N$ in polynomial time. Naturally, the larger $d_p,d_q$, the more LSBs are required. E.g. if $d_p, d_q$ are of size $N^{0.13}$, then we have to know roughly a $\frac 1 5$-fraction of their LSBs, whereas for $d_p, d_q$ of size $N^{0.2}$ we require already knowledge of a $\frac 2 3$-LSB fraction. Eventually, if $d_p, d_q$ are of full size $N^{0.5}$, we have to know all of their bits. Notice that as a side-product of our result we obtain a heuristic deterministic polynomial time factorization algorithm on input $(N,e,d_p,d_q)$.
2020
EUROCRYPT
We propose two heuristic polynomial memory collision finding algorithms for the low Hamming weight discrete logarithm problem in any abelian group $G$. The first one is a direct adaptation of the Becker-Coron-Joux (BCJ) algorithm for subset sum to the discrete logarithm setting. The second one significantly improves on this adaptation for all possible weights using a more involved application of the representation technique together with some new Markov chain analysis. In contrast to other low weight discrete logarithm algorithms, our second algorithm's time complexity interpolates to Pollard's $|G|^{\frac 1 2}$ bound for general discrete logarithm instances. We also introduce a new heuristic subset sum algorithm with polynomial memory that improves on BCJ's $2^{0.72n}$ time bound for random subset sum instances $a_1, \ldots, a_n, t \in \Z_{2^n}$. Technically, we introduce a novel nested collision finding for subset sum -- inspired by the NestedRho algorithm from Crypto '16 -- that recursively produces collisions. We first show how to instantiate our algorithm with run time $2^{0.649n}$. Using further tricks, we are then able to improve its complexity down to $2^{0.645n}$.
2018
CRYPTO
The slightly subexponential algorithm of Blum, Kalai and Wasserman (BKW) provides a basis for assessing LPN/LWE security. However, its huge memory consumption strongly limits its practical applicability, thereby preventing precise security estimates for cryptographic LPN/LWE instantiations.We provide the first time-memory trade-offs for the BKW algorithm. For instance, we show how to solve LPN in dimension k in time $2^{\frac{4}{3} \frac{k}{\log k} }$ and memory $2^{\frac{2}{3} \frac{k}{\log k} }$. Using the Dissection technique due to Dinur et al. (Crypto ’12) and a novel, slight generalization thereof, we obtain fine-grained trade-offs for any available (subexponential) memory while the running time remains subexponential.Reducing the memory consumption of BKW below its running time also allows us to propose a first quantum version QBKW for the BKW algorithm.
2017
TOSC
We study a generalization of the k-list problem, also known as the Generalized Birthday problem. In the k-list problem, one starts with k lists of binary vectors and has to find a set of vectors – one from each list – that sum to the all-zero target vector. In our generalized Approximate k-list problem, one has to find a set of vectors that sum to a vector of small Hamming weight ω. Thus, we relax the condition on the target vector and allow for some error positions. This in turn helps us to significantly reduce the size of the starting lists, which determines the memory consumption, and the running time as a function of ω. For ω = 0, our algorithm achieves the original k-list run-time/memory consumption, whereas for ω = n/2 it has polynomial complexity. As in the k-list case, our Approximate k-list algorithm is defined for all k = 2m,m &gt; 1. Surprisingly, we also find an Approximate 3-list algorithm that improves in the runtime exponent compared to its 2-list counterpart for all 0 &lt; ω &lt; n/2. To the best of our knowledge this is the first such improvement of some variant of the notoriously hard 3-list problem. As an application of our algorithm we compute small weight multiples of a given polynomial with more flexible degree than with Wagner’s algorithm from Crypto 2002 and with smaller time/memory consumption than with Minder and Sinclair’s algorithm from SODA 2009.
2017
PKC
2017
CRYPTO
2017
ASIACRYPT
2015
EUROCRYPT
2012
EUROCRYPT
2012
ASIACRYPT
2011
ASIACRYPT
2010
PKC
2010
CRYPTO
2009
ASIACRYPT
2009
PKC
2008
PKC
2008
ASIACRYPT
2007
CRYPTO
2007
JOFC
2006
ASIACRYPT
2006
PKC
2005
EUROCRYPT
2005
EUROCRYPT
2004
CRYPTO
2004
PKC
2004
PKC
2003
CRYPTO
2002
CRYPTO

Eurocrypt 2020
Asiacrypt 2017
Crypto 2016
Asiacrypt 2016
PKC 2016
Asiacrypt 2015
Crypto 2014
Eurocrypt 2014
PKC 2013
Crypto 2012
PKC 2011
Eurocrypt 2011
Eurocrypt 2010
PKC 2008
Asiacrypt 2007
Eurocrypt 2007
PKC 2006
Eurocrypt 2006