Paper 2021/1548

Just how hard are rotations of ${\mathbb{Z}}^n$? Algorithms and cryptography with the simplest lattice

Abstract

$$We study the computational problem of finding a shortest non-zero vector in a rotation of ${\mathbb{Z}}^n$, which we call $\mathbb{Z}$SVP. It has been a long-standing open problem to determine if a polynomial-time algorithm for $\mathbb{Z}$SVP exists, and there is by now a beautiful line of work showing how to solve it efficiently in certain very special cases. However, despite all of this work, the fastest known algorithm that is proven to solve $$SVP is still simply the fastest known algorithm for solving SVP (i.e., the problem of finding shortest non-zero vectors in arbitrary lattices), which runs in $$ time. We therefore set aside the (perhaps impossible) goal of finding an efficient algorithm for $$SVP and instead ask what else we can say about the problem. E.g., can we find any non-trivial speedup over the best known SVP algorithm? And, if $$SVP actually is hard, then what consequences would follow? Our results are as follows. 1. We show that $$SVP is in a certain sense strictly easier than SVP on arbitrary lattices. In particular, we show how to reduce $$SVP to an approximate version of SVP in the same dimension (in fact, even to approximate unique SVP, for any constant approximation factor). Such a reduction seems very unlikely to work for SVP itself, so we view this as a qualitative separation of $$SVP from SVP. As a consequence of this reduction, we obtain a $$-time algorithm for $$SVP, i.e., the first non-trivial speedup over the best known algorithm for SVP on general lattices. (In fact, this reduction works for a more general class of lattices---semi-stable lattices with not-too-large $$.) 2. We show a simple public-key encryption scheme that is secure if (an appropriate variant of) $$SVP is actually hard. Specifically, our scheme is secure if it is difficult to distinguish (in the worst case) a rotation of $$ from either a lattice with all non-zero vectors longer than $$ or a lattice with smoothing parameter significantly smaller than the smoothing parameter of $$. The latter result has an interesting qualitative connection with reverse Minkowski theorems, which in some sense say that ``$$ has the largest smoothing parameter.'' 3. We show a distribution of bases $$ for rotations of $$ such that, if $$SVP is hard for any input basis, then $$SVP is hard on input $$. This gives a satisfying theoretical resolution to the problem of sampling hard bases for $$, which was studied by Blanks and Miller (PQCrypto, 2021). This worst-case to average-case reduction is also crucially used in the analysis of our encryption scheme. (In recent independent work that appeared as a preprint before this work, Ducas and van Woerden showed essentially the same thing for general lattices (Eurocrypt, 2022), and they also used this to analyze the security of a public-key encryption scheme. Similar ideas also appeared in different contexts in work of Cash, Hofheinz, Kiltz, and Peikert, as well as Aono, Espitau, and Nguyen in different contexts.) 4. We perform experiments to determine how practical basis reduction performs on bases of $$ that are generated in different ways and how heuristic sieving algorithms perform on $$. Our basis reduction experiments complement and add to those performed by Blanks and Miller, as we work with a larger class of algorithms (i.e., larger block sizes) and study the ``provably hard'' distribution of bases described above. Our sieving experiments confirm that heuristic sieving algorithms perform as expected on $$.