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Compositional methods for information-hiding†

Published online by Cambridge University Press:  24 November 2014

KONSTANTINOS CHATZIKOKOLAKIS ,
CATUSCIA PALAMIDESSI  and
CHRISTELLE BRAUN
KONSTANTINOS CHATZIKOKOLAKIS
Affiliation:
LIX, Bâtiment Alan Turing, 1 rue Honoré d'Estienne d'Orves, Campus de l'École Polytechnique, 91120 Palaiseau, France Email: kostas@lix.polytechnique.fr; catuscia@lix.polytechnique.fr; braun@lix.polytechnique.fr
CATUSCIA PALAMIDESSI
Affiliation:
LIX, Bâtiment Alan Turing, 1 rue Honoré d'Estienne d'Orves, Campus de l'École Polytechnique, 91120 Palaiseau, France Email: kostas@lix.polytechnique.fr; catuscia@lix.polytechnique.fr; braun@lix.polytechnique.fr
CHRISTELLE BRAUN
Affiliation:
LIX, Bâtiment Alan Turing, 1 rue Honoré d'Estienne d'Orves, Campus de l'École Polytechnique, 91120 Palaiseau, France Email: kostas@lix.polytechnique.fr; catuscia@lix.polytechnique.fr; braun@lix.polytechnique.fr
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Abstract

Systems concerned with information hiding often use randomization to obfuscate the link between the observables and the information to be protected. The degree of protection provided by a system can be expressed in terms of the probability of error associated with the inference of the secret information. We consider a probabilistic process calculus to specify such systems, and we study how the operators affect the probability of error. In particular, we characterize constructs that have the property of not decreasing the degree of protection, and that can therefore be considered safe in the modular construction of these systems. As a case study, we apply these techniques to the dining cryptographers, and we derive a generalization of Chaum's strong anonymity result.

Type
Paper
Information
Mathematical Structures in Computer Science , Volume 26 , Special Issue 6: Special Issue: Express'10 , September 2016 , pp. 908 - 932
Copyright
Copyright © Cambridge University Press 2014 

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Footnotes

†

This work has been partially supported by the project ANR-12-IS02-001 (PACE), by the project ANR-11-IS02-0002 (LOCALI), by the INRIA Large Scale Initiative CAPPRIS, and by the EU FP7 grant no. 295261 (MEALS). A preliminary version of this work appeared in the proceedings of FOSSACS 2008.

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