Treffer: A One-Time Stegosystem and Applications to Efficient Covert Communication

Title:
A One-Time Stegosystem and Applications to Efficient Covert Communication
Source:
Journal of cryptology. 27(1):23-44
Publisher Information:
New York, NY: Springer, 2014.
Publication Year:
2014
Physical Description:
print, 15 ref
Original Material:
INIST-CNRS
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, United States
Fraunhofer Institute for Algorithms and Scientific Computing, St. Augustin, Germany
Department of Computer Science, Sam Houston State University, Huntsville, TX, United States
ISSN:
0933-2790
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.28569686
Database:
PASCAL Archive

Weitere Informationen

We present the first information-theoretic steganographic protocol with an asymptotically optimal ratio of key length to message length that operates on arbitrary covertext distributions with constant min-entropy. Our results are also applicable to the computational setting: our stegosystem can be composed over a pseudorandom generator to send longer messages in a computationally secure fashion. In this respect our scheme offers a significant improvement in terms of the number of pseudorandom bits generated by the two parties in comparison to previous results known in the computational setting. Central to our approach for improving the overhead for general distributions is the use of combinatorial constructions that have been found to be useful in other contexts for derandomization: almost t-wise independent function families.