Kolmogorov Complexity Characterizes Statistical Zero Knowledge

We show that a decidable promise problem has a non-interactive statistical zero-knowledge proof system if and only if it is randomly reducible via an honest polynomial-time reduction to a promise problem for Kolmogorov-random strings, with a superlogarithmic additive approximation term. This extends work by Saks and Santhanam (CCC 2022). (Saks and Santhanam showed that promise problems that can be reduced in this way to such an approximation of the Kolmogorov-random strings have (possibly interactive) zero-knowledge proof systems, and they did not address the converse implication.) We build on this to give new characterizations of Statistical Zero Knowledge SZK , as well as the related classes NISZK L and SZK L .

Authors

Institutions

Publication Details

Journal
ACM Transactions on Computation Theory
Published
2026-02-10
DOI
https://doi.org/10.1145/3795688
Primary Topic
Computability, Logic, AI Algorithms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Kolmogorov Complexity Characterizes Statistical Zero Knowledge

Harsha Tirumala, Shuichi Hirahara, Eric Allender
ACM Transactions on Computation Theory
Computability, Logic, AI Algorithms
article

Kolmogorov Complexity Characterizes Statistical Zero Knowledge

Harsha Tirumala, Shuichi Hirahara, Eric Allender
article en

Abstract

We show that a decidable promise problem has a non-interactive statistical zero-knowledge proof system if and only if it is randomly reducible via an honest polynomial-time reduction to a promise problem for Kolmogorov-random strings, with a superlogarithmic additive approximation term. This extends work by Saks and Santhanam (CCC 2022). (Saks and Santhanam showed that promise problems that can be reduced in this way to such an approximation of the Kolmogorov-random strings have (possibly interactive) zero-knowledge proof systems, and they did not address the converse implication.) We build on this to give new characterizations of Statistical Zero Knowledge SZK , as well as the related classes NISZK L and SZK L .

ACM Transactions on Computation Theory
Rutgers, The State University of New Jersey (US), National Institute of Informatics (JP), Computing Center (RU)
Openalex Percentile: Top 59%
Computability, Logic, AI Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.