Lattice Problems beyond Polynomial Time
Divesh Aggarwal,Huck Bennett,Zvika Brakerski,Alexander Golovnev,Zeyong Li,Spencer James Peters,Noah Stephens-Davidowitz,Vinod Vaikuntanathan +7 more
- 21 Nov 2022
TL;DR: In this article , the complexity of lattice problems in a world where algorithms, reductions, and protocols can run in superpolynomial time was studied and two protocols and two worst-case to average-case reductions were shown.
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Abstract: We study the complexity of lattice problems in a world where algorithms, reductions, and protocols can run in superpolynomial time. Specifically, we revisit four foundational results in this context—two protocols and two worst-case to average-case reductions. We show how to improve the approximation factor in each result by a factor of roughly √n/logn when running the protocol or reduction in 2є n time instead of polynomial time, and we show a novel protocol with no polynomial-time analog. Our results are as follows. (1) We show a worst-case to average-case reduction proving that secret-key cryptography (specifically, collision-resistant hash functions) exists if the (decision version of the) Shortest Vector Problem (SVP) cannot be approximated to within a factor of Õ(√n) in 2є n time. This extends to our setting Ajtai’s celebrated polynomial-time reduction for the Short Integer Solutions (SIS) problem (1996),which showed (after improvements by Micciancio and Regev (2004, 2007)) that secret-key cryptography exists if SVP cannot be approximated to within a factor of Õ(n) in polynomial time. (2) We show another worst-case to average-case reduction proving that public-key cryptography exists if SVP cannot be approximated to within a factor of Õ(n) in 2є n time. This extends Regev’s celebrated polynomial-time reduction for the Learning with Errors (LWE) problem (2005, 2009), which achieved an approximation factor of Õ(n1.5). In fact, Regev’s reduction is quantum, but we prove our result under a classical reduction, generalizing Peikert’s polynomial-time classical reduction (2009), which achieved an approximation factor of Õ(n2). (3) We show that the (decision version of the) Closest Vector Problem (CVP) with a constant approximation factor has a coAM protocol with a 2є n-time verifier. We prove this via a (very simple) generalization of the celebrated polynomial-time protocol due to Goldreich and Goldwasser (1998, 2000). It follows that the recent series of 2є n-time and even 2(1−є)n-time hardness results for CVP cannot be extended to large constant approximation factors γ unless AMETH is false. We also rule out 2(1−є)n-time lower bounds for any constant approximation factor γ > √2, under plausible complexity-theoretic assumptions. (These results also extend to arbitrary norms, with different constants.) (4) We show that O(√logn)-approximate SVP has a coNTIME protocol with a 2є n-time verifier. Here, the analogous (also celebrated!) polynomial-time result is due to Aharonov and Regev (2005), who showed a polynomial-time protocol achieving an approximation factor of √n (for both SVP and CVP, while we only achieve this result for CVP). This result implies similar barriers to hardness, with a larger approximation factor under a weaker complexity-theoretic conjectures (as does the next result). (5) Finally, we give a novel coMA protocol for constant-factor-approximate CVP with a 2є n-time verifier. Unlike our other results, this protocol has no known analog in the polynomial-time regime. All of the results described above are special cases of more general theorems that achieve time-approximation factor tradeoffs. In particular, the tradeoffs for the first four results smoothly interpolate from the polynomial-time results in prior work to our new results in the exponential-time world.
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Citations
Why we couldn't prove SETH hardness of the Closest Vector Problem for even norms, and of the Subset Sum Problem!
TL;DR: For the (cid:96) 2 norm, it was shown in this paper that lattice problems in the Euclidean norm are easier than lattice in other norms, which is the first result that shows a separation between these problems.
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- 01 Jan 2023
TL;DR: Fine-grained hardness of Unique Shortest Vector Problem is NP-hard in subexponential setting.
Why we couldn’t prove SETH hardness of the Closest Vector Problem for even norms!
Divesh Aggarwal,Rajendra Kumar +1 more
- 06 Nov 2023
TL;DR: The results show that proving SETH hardness of CVP in the $\ell_{p}$ norm for even norms is significantly harder than proving SETH hardness in other norms.
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TL;DR: Researchers propose two algorithms for Matrix Multiplication Verification Problem (MMV) when AB - C is sparse, achieving faster deterministic and randomized times than existing methods, while also exploring the complexity of MMV and its variants under various hypotheses.
The Complexity of the Shortest Vector Problem
TL;DR: In this article , the authors present known results and open questions related to the complexity of the Shortest Vector Problem (SVP) on point lattices, and present a survey.
References
Factoring Polynomials with Rational Coefficients
TL;DR: This paper presents a polynomial-time algorithm to solve the following problem: given a non-zeroPolynomial fe Q(X) in one variable with rational coefficients, find the decomposition of f into irreducible factors in Q (X).
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TL;DR: A (classical) public-key cryptosystem whose security is based on the hardness of the learning problem, which is a reduction from worst-case lattice problems such as GapSVP and SIVP to a certain learning problem that is quantum.
2.3K
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- 17 May 2008
TL;DR: In this article, the authors show how to construct a variety of "trapdoor" cryptographic tools assuming the worst-case hardness of standard lattice problems (such as approximating the length of the shortest nonzero vector to within certain polynomial factors).
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- 01 Jul 1996
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