Abstract
Most computational systems share two basic properties: first they process information, second they allow for observations of this processing to be made. For example a program will typically process the inputs and allows its output to be observed on a screen. In a distributed system each unit processes information and will allow some observation to be made by the environment or other units, for example by message passing. In an election voters cast their vote, officials count the votes and the public can observe the election result.
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References
Backes, M., Köpf, B., Rybalchenko, A.: Automatic Discovery and Quantification of Information Leaks. In: Proc. 30th IEEE Symposium on Security and Privacy S& P ’09 (2009)
Chatzikokolakis, K., Palamidessi, C., Panangaden, P.: Anonymity protocols as noisy channels. Information and Computation 206(2-4), 378–401 (2008)
Chen, H., Malacaria, P.: Quantifying Maximal Loss of Anonymity in Protocols. In: Proceedings ACM Symposium on Information, Computer and Communication Security (2009)
Clark, D., Hunt, S., Malacaria, P.: A static analysis for quantifying information flow in a simple imperative language. Journal of Computer Security 15(3) (2007)
Clark, D., Hunt, S., Malacaria, P.: Quantitative information flow, relations and polymorphic types. Journal of Logic and Computation, Special Issue on Lambda-calculus, type theory and natural language 18(2), 181–199 (2005)
Kpf, B., Rybalchenko, A.: Approximation and Randomization for Quantitative Information-Flow Analysis. In: Proceedings of Computer Security Foundations Symposium 2010 (2010)
Heusser, J., Malacaria, P.: Applied Quantitative Information Flow and Statistical Databases. In: Proceedings of Workshop on Formal Aspects in Security and Trust, FAST 2009 (2009)
Goguen, J.A., Meseguer, J.: Security policies and security model. In: Proceedings of the 1982 IEEE Computer Society Symposium on Security and Privacy (1982)
Malacaria, P.: Assessing security threats of looping constructs. In: Proc. ACM Symposium on Principles of Programming Language (2007)
Malacaria, P.: Risk Assessment of Security Threats for Looping Constructs. Journal Of Computer Security 18(2) (2010)
McCamant, S., Ernst, M.D.: Quantitative information flow as network flow capacity. In: PLDI 2008, Proceedings of the ACM SIGPLAN 2008, Conference on Programming Language Design and Implementation, Tucson, AZ, USA (2008)
Mu, C., Clark, D.: An Abstraction Quantifying Information Flow over Probabilistic Semantics. In: Workshop on Quantitative Aspects of Programming Languages (QAPL), ETAPS (2009)
Smith, G.: On the Foundations of Quantitative Information Flow. In: de Alfaro, L. (ed.) FOSSACS 2009. LNCS, vol. 5504, pp. 288–302. Springer, Heidelberg (2009)
Yasuoka, H., Terauchi, T.: Quantitative Information Flow - Verification Hardness and Possibilities. In: Proceedings of Computer Security Foundations Symposium (2010)
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Malacaria, P. (2010). Quantitative Information Flow: From Theory to Practice?. In: Touili, T., Cook, B., Jackson, P. (eds) Computer Aided Verification. CAV 2010. Lecture Notes in Computer Science, vol 6174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14295-6_3
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DOI: https://doi.org/10.1007/978-3-642-14295-6_3
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