Online-Extractability in the Quantum Random-Oracle Model

Jelle Don*, Serge Fehr, Christian Majenz, Christian Schaffner

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

We show the following generic result: When a quantum query algorithm in the quantum random-oracle model outputs a classical value t that is promised to be in some tight relation with H(x) for some x, then x can be efficiently extracted with almost certainty. The extraction is by means of a suitable simulation of the random oracle and works online, meaning that it is straightline, i.e., without rewinding, and on-the-fly, i.e., during the protocol execution and (almost) without disturbing it. The technical core of our result is a new commutator bound that bounds the operator norm of the commutator of the unitary operator that describes the evolution of the compressed oracle (which is used to simulate the random oracle above) and of the measurement that extracts x. We show two applications of our generic online extractability result. We show tight online extractability of commit-and-open Σ -protocols in the quantum setting, and we offer the first complete post-quantum security proof of the textbook Fujisaki-Okamoto transformation, i.e., without adjustments to facilitate the proof, including concrete security bounds.

Original languageEnglish
Title of host publicationProceedings of 41st Annual International Conference on the Theory and Applications of Cryptographic Techniques
EditorsOrr Dunkelman, Stefan Dziembowski
PublisherSpringer
Publication date2022
Pages677-706
ISBN (Print)9783031070815
DOIs
Publication statusPublished - 2022
Event41st Annual International Conference on the Theory and Applications of Cryptographic Techniques - Trondheim, Norway
Duration: 30 May 20223 Jun 2022

Conference

Conference41st Annual International Conference on the Theory and Applications of Cryptographic Techniques
Country/TerritoryNorway
CityTrondheim
Period30/05/202203/06/2022
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13277 LNCS
ISSN0302-9743

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