Refinements of the k-tree Algorithm for the Generalized Birthday Problem

We study two open problems proposed by Wagner in his seminal work on the generalized birthday problem. First, with the use of multicollisions, we improve Wagner\u27s $3$-tree algorithm. The new 3-tree only slightly outperforms Wagner\u27s 3-tree, however, in some applications this suffices, and as a proof of concept, we apply the new algorithm to slightly reduce the security of two CAESAR proposals. Next, with the use of multiple collisions based on Hellman\u27s table, we give improvements to the best known time-memory tradeoffs for the k-tree. As a result, we obtain the a new tradeoff curve T^2 \cdot M^{\lg k -1} = k \cdot N. For instance, when k=4, the tradeoff has the form T^2 M = 4 \cdot N

Another Look at the Cost of Cryptographic Attacks

This paper makes the case for considering the cost of cryptographic attacks as the main measure of their efficiency, instead of their time complexity. This allows, in our opinion, a more realistic assessment of the "risk" these attacks represent. This is half-and-half a position and a technical paper. Cryptographic attacks described in the literature are rarely implemented. Most exist only "on paper", and their main characteristic is that their estimated time complexity is small enough to break a given security property. However, when a cryptanalyst actually considers implementing an attack, she soon realizes that there is more to the story than time complexity. For instance, Wiener has shown that breaking the double-DES costs 2 6n/5 , asymptotically more than exhaustive search on n bits. We put forward the asymptotic cost of cryptographic attacks as a measure of their practicality. We discuss the shortcomings of the usual computational model and propose a simple abstract cryptographic machine on which it is easy to estimate the cost. We then study the asymptotic cost of several relevant algorithm: collision search, the three-list birthday problem (3XOR) and solving multivariate quadratic polynomial equations. We find that some smart algorithms cost much more than what their time complexity suggest, while naive and simple algorithms may cost less. Some algorithms can be tuned to reduce their cost (this increases their time complexity). Foreword A celebrated High Performance Computing paper entitled "Hitting the Memory Wall: Implications of the Obvious" [47] opens with these words: This brief note points out something obvious-something the authors "knew" without really understanding. With apologies to those who did understand, we offer it to those others who, like us, missed the point. We would like to do the same-but this note is not so short

The Approximate k-List Problem

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 > 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 < ω < 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

Attaques Génériques sur des BBB MACs

International audienc

Quantum Algorithms for the k-xor Problem

International audienceThe k-xor (or generalized birthday) problem is a widely studied question with many applications in cryptography. It aims at finding k elements of n bits, drawn at random, such that the xor of all of them is 0. The algorithms proposed by Wagner more than fifteen years ago remain the best known classical algorithms for solving them, when disregarding logarithmic factors. In this paper we study these problems in the quantum setting, when considering that the elements are created by querying a random function (or k random functions) H : {0, 1} n → {0, 1} n. We consider two scenarios: in one we are able to use a limited amount of quantum memory (i.e. a number O(n) of qubits, the same as the one needed by Grover's search algorithm), and in the other we consider that the algorithm can use an exponential amount of qubits. Our newly proposed algorithms are of general interest. In both settings, they provide the best known quantum time complexities. In particular, we are able to considerately improve the 3-xor algorithm: with limited qubits, we reach a complexity considerably better than what is currently possible for quantum collision search. Furthermore, when having access to exponential amounts of quantum memory, we can take this complexity below O(2 n/3), the well-known lower bound of quantum collision search, clearly improving the best known quantum time complexity also in this setting. We illustrate the importance of these results with some cryptographic applications

T5: Hashing Five Inputs with Three Compression Calls

Given $2n$-to-$n$ compression functions $h_1,h_2,h_3$, we build a new $5n$-to-$n$ compression function $\mathrm{T}_5$, using only $3$ compression calls: $\mathrm{T}_5(m_1, m_2, m_3, m_4, m_5) := h_3( h_1(m_1, m_2)\oplus m_5, h_2(m_3, m_4)\oplus m_5) \oplus m_5$. We prove that this construction matches Stam\u27s bound, by providing $\tilde{O}(q^2/2^n)$ collision security and $O(q^3/2^{2n}+ nq/2^n)$ preimage security (the latter term dominates in the region of interest, when $q<2^{n/2}$). In particular, it provides birthday security for hashing $5$ inputs using three $2n$-to-$n$ compression calls, instead of only $4$ inputs in prior constructions. Thus, we get a sequential variant of the Merkle-Damgård (MD) hashing, where $t$ message blocks are hashed using only $3t/4$ calls to the $2n$-to-$n$ compression functions; a $25\%$ saving over traditional hash function constructions. This time reduces to $t/4$ (resp. $t/2$) sequential calls using $3$ (resp. $2$) parallel execution units; saving a factor of $4$ (resp. $2$) over the traditional MD-hashing, where parallelism does not help to process one message. We also get a novel variant of a Merkle tree, where $t$ message blocks can be processed using $0.75(t-1)$ compression function calls and depth $0.86 \log_2 t$, thereby saving $25\%$ in the number of calls and $14\%$ in the update time over Merkle trees. We provide two modes for a local opening of a particular message block: conservative and aggressive. The former retains the birthday security, but provides longer proofs and local verification time than the traditional Merkle tree. For the aggressive variant, we reduce the proof length to a $29\%$ overhead compared to Merkle trees ($1.29\log_2 t$ vs $\log_2 t$), but the verification time is now $14\%$ faster ($0.86\log_2 t$ vs $\log_2 t$). However, birthday security is only shown under a plausible conjecture related to the 3-XOR problem, and only for the (common, but not universal) setting where the root of the Merkle tree is known to correspond to a valid $t$-block message

Optimal Merging in Quantum k-xor and k-sum Algorithms

International audienceThe k-xor or Generalized Birthday Problem aims at finding, given k lists of bit-strings, a k-tuple among them XORing to 0. If the lists are unbounded, the best classical (exponential) time complexity has withstood since Wagner's CRYPTO 2002 paper. If the lists are bounded (of the same size) and such that there is a single solution, the dissection algorithms of Dinur et al. (CRYPTO 2012) improve the memory usage over a simple meet-in-the-middle. In this paper, we study quantum algorithms for the k-xor problem. With unbounded lists and quantum access, we improve previous work by Grassi et al. (ASIACRYPT 2018) for almost all k. Next, we extend our study to lists of any size and with classical access only. We define a set of "merging trees" which represent the best known strategies for quantum and classical merging in k-xor algorithms, and prove that our method is optimal among these. Our complexities are confirmed by a Mixed Integer Linear Program that computes the best strategy for a given k-xor problem. All our algorithms apply also when considering modular additions instead of bitwise xors. This framework enables us to give new improved quantum k-xor algorithms for all k and list sizes. Applications include the subset-sum problem, LPN with limited memory and the multiple-encryption problem