Modeling and Verifying Security Protocols with the Applied Pi Calculus and ProVerif

TLDR: This survey presents an overview of the research on ProVerif, an automatic symbolic protocol verifier that automatically translates this protocol description into Horn clauses and determines whether the desired security properties hold by resolution on these clauses.

Abstract: ProVerif is an automatic symbolic protocol verifier It supports a wide range of cryptographic primitives, defined by rewrite rules or by equations It can prove various security properties: secrecy, authentication, and process equivalences, for an unbounded message space and an unbounded number of sessions It takes as input a description of the protocol to verify in a dialect of the applied pi calculus, an extension of the pi calculus with cryptography It automatically translates this protocol description into Horn clauses and determines whether the desired security properties hold by resolution on these clauses This survey presents an overview of the research on ProVerif

Full text

Modeling and Verifying
Security Protocols with the
Applied Pi Calculus and
ProVerif
Full text available at: http://dx.doi.org/10.1561/3300000004

Full text available at: http://dx.doi.org/10.1561/3300000004

Modeling and Verifying Security
Protocols with the Applied Pi
Calculus and ProVerif
Bruno Blanchet
INRIA Paris, France
Bruno.Blanchet@inria.fr
Boston — Delft
Full text available at: http://dx.doi.org/10.1561/3300000004

Foundations and Trends
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in Privacy and Secruity
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The preferred citation for this publication is
B. Blanchet. Modeling and Verifying Security Protocols with the Applied Pi Calculus
and ProVerif. Foundations and Trends
R

in Privacy and Secruity, vol. 1, no. 1-2,
pp. 1–135, 2016.
ISBN: 978-1-68083-207-5
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 2016 B. Blanchet
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Full text available at: http://dx.doi.org/10.1561/3300000004

Foundations and Trends
R

in Privacy and
Secruity
Volume 1, Issue 1-2, 2016
Editorial Board
Editor-in-Chief
Anupam Datta
Carnegie Mellon University
United States
Jeannette Wing
Microsoft Research
United States
Editors
Martín Abadi
Google and UC Santa Cruz
Michael Backes
Saarland University
Dan Boneh
Stanford University
Véronique Cortier
LORIA, CNRS
Lorrie Cranor
Carnegie Mellon University
Cédric Fournet
Microsoft Research
Virgil Gligor
Carnegie Mellon University
Jean-Pierre Hubaux
EPFL
Deirdre Mulligan
UC Berkeley
Andrew Myers
Cornell University
Helen Nissenbaum
New York University
Michael Reiter
University of North Carolina
Shankar Sastry
UC Berkeley
Dawn Song
UC Berkeley
Daniel Weitzner
MIT
Full text available at: http://dx.doi.org/10.1561/3300000004