J. K. Millen, Covert Channel Capacity, 1987 IEEE Symposium on Security and Privacy, pp.60-66, 1987.
DOI : 10.1109/SP.1987.10013

J. Mclean, Security models and information flow, Proceedings. 1990 IEEE Computer Society Symposium on Research in Security and Privacy, pp.180-189, 1990.
DOI : 10.1109/RISP.1990.63849

J. W. Gray and . Iii, Toward a mathematical foundation for information flow security, IEEE Symposium on Security and Privacy, pp.21-35, 1991.

D. Clark, S. Hunt, and P. Malacaria, Quantitative Analysis of the Leakage of Confidential Data, Proc. Workshop on Quantitative Aspects of Programming Languages, pp.238-251, 2001.
DOI : 10.1016/S1571-0661(04)00290-7

M. Boreale, Quantifying information leakage in process calculi, Proc. ICALP '06, pp.119-131, 2006.

P. Malacaria, Assessing security threats of looping constructs, Proc. 34th Symposium on Principles of Programming Languages (POPL '07), pp.225-235, 2007.

B. Köpf and D. Basin, An information-theoretic model for adaptive side-channel attacks, Proceedings of the 14th ACM conference on Computer and communications security , CCS '07, pp.286-296, 2007.
DOI : 10.1145/1315245.1315282

K. Chatzikokolakis, C. Palamidessi, and P. Panangaden, Anonymity protocols as noisy channels, Information and Computation, vol.206, issue.2-4, pp.378-401, 2008.
DOI : 10.1016/j.ic.2007.07.003

URL : https://hal.archives-ouvertes.fr/inria-00349225

G. Smith, On the Foundations of Quantitative Information Flow, Proc. 12th International Conference on Foundations of Software Science and Computational Structures (FoSSaCS '09), ser. Lecture Notes in Computer Science, L. de Alfaro, pp.288-302, 2009.
DOI : 10.1137/060651380

M. R. Clarkson and F. B. Schneider, Quantification of integrity, Proc. 23nd IEEE Computer Security Foundations Symposium (CSF '10), pp.28-43, 2010.

M. Boreale, F. Pampaloni, and M. Paolini, Asymptotic information leakage under one-try attacks, Proc. FOSSACS '11, pp.396-410, 2011.
DOI : 10.1007/978-3-642-19805-2_27

C. Braun, K. Chatzikokolakis, and C. Palamidessi, Quantitative Notions of Leakage for One-try Attacks, Proc. 25th Conference on Mathematical Foundations of Programming Semantics ser. ENTCS, pp.75-91, 2009.
DOI : 10.1016/j.entcs.2009.07.085

URL : https://hal.archives-ouvertes.fr/inria-00424852

G. Smith, Quantifying Information Flow Using Min-Entropy, 2011 Eighth International Conference on Quantitative Evaluation of SysTems, pp.159-167, 2011.
DOI : 10.1109/QEST.2011.31

H. Yasuoka and T. Terauchi, Quantitative Information Flow - Verification Hardness and Possibilities, 2010 23rd IEEE Computer Security Foundations Symposium, pp.15-27, 2010.
DOI : 10.1109/CSF.2010.9

URL : http://arxiv.org/abs/1004.0062

P. Malacaria, Algebraic foundations for information theoretical , probabilistic and guessability measures of information flow, 1101.

J. Landauer and T. Redmond, A lattice of information, [1993] Proceedings Computer Security Foundations Workshop VI, pp.65-70, 1993.
DOI : 10.1109/CSFW.1993.246638

B. Espinoza and G. Smith, Min-Entropy Leakage of Channels in Cascade, Proc. Formal Aspects of Security and Trust (FAST 2011), ser. Lecture Notes in Computer Science, 2011.
DOI : 10.1007/978-3-642-29420-4_5

G. Parmigiani and L. Inoue, Decision Theory: Principles and Approaches, 2009.
DOI : 10.1002/9780470746684

A. Zellner, Bayesian Estimation and Prediction Using Asymmetric Loss Functions, Journal of the American Statistical Association, vol.24, issue.394, pp.446-451, 1986.
DOI : 10.1080/01621459.1986.10478289

F. Schorfheide, Loss function-based evaluation of DSGE models, Journal of Applied Econometrics, vol.8, issue.6, pp.645-670, 2000.
DOI : 10.1002/jae.582

J. Pan and J. Pan, A Comparative Study of Various Loss Functions in the Economic Tolerance Design, 2006 IEEE International Conference on Management of Innovation and Technology, pp.783-787, 2006.
DOI : 10.1109/ICMIT.2006.262327

M. A. Ian, H. Witten, and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2011.

T. G. Dietterich, Machine Learning for Sequential Data: A Review, Structural, Syntactic, and Statistical Pattern Recognition, pp.15-30, 2002.
DOI : 10.1007/3-540-70659-3_2

]. A. Ghosh, T. Roughgarden, and M. Sundararajan, Universally utility-maximizing privacy mechanisms, Proc. 41st ACM Symposium on Theory of Computing, pp.351-360, 2009.
DOI : 10.1145/1536414.1536464

URL : http://arxiv.org/abs/0811.2841

M. Boreale, F. Pampaloni, and M. Paolini, Quantitative Information Flow, with a View, Proc. ESORICS '11, 2011.
DOI : 10.1007/978-3-642-23822-2_32

A. Mciver, L. Meinicke, and C. Morgan, Compositional Closure for Bayes Risk in Probabilistic Noninterference, Proc. ICALP'10, pp.223-235, 2010.
DOI : 10.1007/978-3-642-14162-1_19

M. Clarkson, A. Myers, and F. Schneider, Belief in Information Flow, 18th IEEE Computer Security Foundations Workshop (CSFW'05), pp.31-45, 2005.
DOI : 10.1109/CSFW.2005.10

S. Hamadou, V. Sassone, and C. Palamidessi, Reconciling Belief and Vulnerability in Information Flow, 2010 IEEE Symposium on Security and Privacy, pp.79-92, 2010.
DOI : 10.1109/SP.2010.13

URL : https://hal.archives-ouvertes.fr/inria-00548007