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One-way functions and a conditional variant of MKTP
Conference paper   Open access   Peer reviewed

One-way functions and a conditional variant of MKTP

Eric Allender, Mahdi Cheraghchi, Dimitrios Myrisiotis, Harsha Tirumala and Ilya Volkovoch
41st IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2021), Vol.213, pp.7:1-7:19
Leibniz International Proceedings in Informatics (LIPIcs), Schloss Dagstuhl -- Leibniz-Zentrum fur Informatik
IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2021) International Proceedings in Informatics (LIPIcs) , 41
2021
DOI:
https://doi.org/10.7282/00000157

Abstract

Kolmogorov Complexity KT Complexity Conditional KT-complexity One-Way Functions Average-case Hardness Pseudorandom Generators NP-completeness Reductions
One-way functions (OWFs) are central objects of study in cryptography and computational complexity theory. In a seminal work, Liu and Pass (FOCS 2020) proved that the average-case hardness of computing time-bounded Kolmogorov complexity is equivalent to the existence of OWFs. It remained an open problem to establish such an equivalence for the average-case hardness of some natural NP-complete problem. In this paper, we make progress on this question by studying a conditional variant of the Minimum KT-complexity Problem (MKTP), which we call McKTP, as follows. 1. First, we prove that if McKTP is average-case hard on a polynomial fraction of its instances, then there exist OWFs. 2. Then, we observe that McKTP is NP-complete under polynomial-time randomized reductions. 3. Finally, we prove that the existence of OWFs implies the nontrivial average-case hardness of McKTP. Thus the existence of OWFs is inextricably linked to the average-case hardness of this NP-complete problem. In fact, building on recent results of Ren and Santhanam (CCC 2021), we show that McKTP is hard-on-average if and only if there are logspace-computable OWFs.
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https://doi.org/10.4230/LIPIcs.FSTTCS.2021.7View
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