$k$-SUM in the Sparse Regime

Abstract

In the average-case $k$-SUM problem, given $r$ integers chosen uniformly at random from $\{0,\ldots,M-1\}$, the objective is to find a solution set of $k$ numbers that sum to $0$ modulo $M$. In the dense regime of $M \leq r^k$, where solutions exist with high probability, the complexity of these problems is well understood. Much less is known in the sparse regime of $M\gg r^k$, where solutions are unlikely to exist. In this work, we initiate the study of the sparse regime for $k$-SUM and its variant $k$-XOR, especially their planted versions, where a random solution is planted in a randomly generated instance and has to be recovered. We provide evidence for the hardness of these problems and suggest new applications to cryptography. Our contributions are summarized below. Complexity. First we study the complexity of these problems in the sparse regime and show: - Conditional Lower Bounds. Assuming established conjectures about the hardness of average-case (non-planted) $k$-SUM/$k$-XOR when $M = r^k$, we provide non-trivial lower bounds on the running time of algorithms for planted $k$-SUM when $r^k\leq M\leq r^{2k}$. - Hardness Amplification. We show that for any $M \geq r^k$, if an algorithm running in time $T$ solves planted $k$-SUM/$k$-XOR with success probability $\Omega(1/\text{polylog}(r))$, then there is an algorithm running in time $\tilde{O}(T)$ that solves it with probability $(1-o(1))$. This in particular implies hardness amplification for 3-SUM over the integers, which was not previously known. Technically, our approach departs significantly from existing approaches to hardness amplification, and relies on the locality of the solution together with the group structure inherent in the problem. Additionally, it enables us to assume only mild hardness of these problems for use in applications. - New Reductions and Algorithms. We provide reductions for $k$-SUM/$k$-XOR from search to decision, as well as worst-case and average-case reductions to the Subset Sum problem from $k$-SUM. Additionally, we present a new algorithm for average-case $k$-XOR that is faster than known worst-case algorithms at low densities. Cryptography. We show that by additionally assuming mild hardness of $k$-XOR, we can construct Public Key Encryption (PKE) from a weaker variant of the Learning Parity with Noise (LPN) problem than was known before. In particular, such LPN hardness does not appear to imply PKE on its own -- this suggests that $k$-XOR/$k$-SUM can be used to bridge minicrypt and cryptomania in some cases, and may be applicable in other settings in cryptography