Abstract
Proper learning from positive samples is a basic ingredient for designing secure steganographic systems for unknown covertext channels. In addition, security requirements imply that the hypothesis should not contain false positives. We present such a learner for k-term DNF formulas for the uniform distribution and a generalization to q-bounded distributions. We briefly also describe how these results can be used to design a secure stegosystem.
Original language | English |
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Title of host publication | Algorithms and Computation |
Editors | Khaled Elbassioni, Kazuhisa Makino |
Number of pages | 12 |
Volume | 9472 |
Publisher | Springer Verlag |
Publication date | 27.11.2015 |
Pages | 151-162 |
ISBN (Print) | 978-3-662-48970-3 |
ISBN (Electronic) | 978-3-662-48971-0 |
DOIs | |
Publication status | Published - 27.11.2015 |
Event | ISAAC 2015 - Nagoya Marriott Associa Hotel , Nagoya, Japan Duration: 09.12.2015 → 11.12.2015 http://www.al.cm.is.nagoya-u.ac.jp/isaac2015/ |