Quantifying opacity
From MaRDI portal
Publication:5740626
DOI10.1017/S0960129513000637zbMath1361.68117arXiv1301.6799MaRDI QIDQ5740626
Mathieu Sassolas, Béatrice Bérard, John Mullins
Publication date: 27 July 2016
Published in: Mathematical Structures in Computer Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1301.6799
Formal languages and automata (68Q45) Probability in computer science (algorithm analysis, random structures, phase transitions, etc.) (68Q87)
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Cites Work
- Opacity of discrete event systems and its applications
- Quantifying information leakage in process calculi
- A logic for reasoning about time and reliability
- Anonymity protocols as noisy channels
- Model checking of probabilistic and nondeterministic systems
- Asymptotic Information Leakage under One-Try Attacks
- Quantitative Information Flow, with a View
- Computing the Leakage of Information-Hiding Systems
- Information Flow in Interactive Systems
- Compositional Closure for Bayes Risk in Probabilistic Noninterference
- Preserving Secrecy Under Refinement
- Markov decision processes and regular events