Computational soundness, co-induction, and encryption cycles

7Citations
Citations of this article
34Readers
Mendeley users who have this article in their library.

Abstract

We analyze the relation between induction, co-induction and the presence of encryption cycles in the context of computationally sound symbolic equivalence of cryptographic expressions. Our main finding is that the use of co-induction in the symbolic definition of the adversarial knowledge allows to prove soundness results without the need to require syntactic restrictions, like the absence of encryption cycles, common to most previous work in the area. Encryption cycles are relevant only to the extent that the key recovery function associated to acyclic expressions can be shown to have a unique fixed point. So, when a cryptographic expression has no encryption cycles, the inductive (least fixed point) and co-inductive (greatest fixed point) security definitions produce the same results, and the computational soundness of the inductive definitions for acyclic expressions follows as a special case of the soundness of the co-inductive definition. © 2010 Springer-Verlag.

References Powered by Scopus

On the Security of Public Key Protocols

4339Citations
N/AReaders
Get full text

Probabilistic encryption

2605Citations
N/AReaders
Get full text

An efficient system for non-transferable anonymous credentials with optional anonymity revocation

859Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Symbolic security of garbled circuits

6Citations
N/AReaders
Get full text

A survey on computationally sound formal analysis of cryptographic protocols

5Citations
N/AReaders
Get full text

SoK: Learning with Errors, Circular Security, and Fully Homomorphic Encryption

2Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Micciancio, D. (2010). Computational soundness, co-induction, and encryption cycles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6110 LNCS, pp. 362–380). https://doi.org/10.1007/978-3-642-13190-5_19

Readers over time

‘11‘13‘14‘15‘16‘17‘18‘19‘20‘21‘23036912

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 23

77%

Researcher 3

10%

Professor / Associate Prof. 2

7%

Lecturer / Post doc 2

7%

Readers' Discipline

Tooltip

Computer Science 24

80%

Physics and Astronomy 2

7%

Engineering 2

7%

Mathematics 2

7%

Save time finding and organizing research with Mendeley

Sign up for free
0