Exact Hidden Paths in Noisy High Dimensional Path Spaces
Victor Duarte Melo

TL;DR
This paper develops a mathematical and cryptographic framework for the exact recovery of noisy hidden paths in high-dimensional discrete spaces, inspired by path integral concepts.
Contribution
It introduces formal recovery notions and analyzes attack surfaces, laying groundwork for future cryptographic applications based on hidden path recovery.
Findings
Formalized various recovery notions including exact and approximate recovery.
Analyzed multiple attack strategies such as lattice, quantum, and SAT-based methods.
Highlighted the distinction between coarse geometric recovery and exact microscopic path recovery.
Abstract
We introduce a mathematical and cryptographic framework for exact recovery of noisy hidden paths in high dimensional discrete path spaces. The work is inspired by the path integral viewpoint, where global quantities arise from contributions over many possible trajectories. Instead of approximating a global path sum, we study the inverse problem of recovering one exact hidden trajectory from incomplete, noisy, projected, and aggregated observables. The hidden object is a planted discrete path whose transitions may include macro steps, microscopic perturbations, and discrete noise. Public information is represented by large observable vectors rather than short hash digests, since excessive compression would bound the effective recovery problem by the digest size. We formalize several recovery notions, including planted exact recovery, arbitrary witness recovery, canonical recovery,…
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