Pseudorandomness Properties of Random Reversible Circuits
William Gay, William He, Nicholas Kocurek, Ryan O'Donnell

TL;DR
This paper demonstrates that random reversible circuits with specific depth and architecture can produce permutations with strong pseudorandom properties, useful for cryptography, by analyzing their spectral gaps and combining one- and two-dimensional constructions.
Contribution
It introduces a simple, practical construction of approximately $k$-wise independent permutations using random reversible circuits in fixed architectures, with provable security guarantees.
Findings
Random circuits of depth ~√n produce approximate $k$-wise independent permutations.
Spectral gap analysis of Markov chains induced by 3-bit gates.
Two-dimensional lattice arrangements enhance permutation independence.
Abstract
Motivated by practical concerns in cryptography, we study pseudorandomness properties of permutations on computed by random circuits made from reversible -bit gates (permutations on ). Our main result is that a random circuit of depth , with each layer consisting of random gates in a fixed two-dimensional nearest-neighbor architecture, yields approximate -wise independent permutations. Our result can be seen as a particularly simple/practical block cipher construction that gives provable statistical security against attackers with access to ~input-output pairs within few rounds. The main technical component of our proof consists of two parts: 1. We show that the Markov chain on -tuples of -bit strings induced by a single random -bit one-dimensional nearest-neighbor gate has spectral gap at least…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum-Dot Cellular Automata · Computability, Logic, AI Algorithms
