Abstract
Secure information flow is the problem of ensuring that the information made publicly available by a computational system does not leak information that should be kept secret. Since it is practically impossible to avoid leakage entirely, in recent years there has been a growing interest in considering the quantitative aspects of information flow, in order to measure and compare the amount of leakage. Information theory is widely regarded as a natural framework to provide firm foundations to quantitive information flow. In this notes we review the two main information-theoretic approaches that have been investigated: the one based on Shannon entropy, and the one based on Rényi min-entropy. Furthermore, we discuss some applications in the area of privacy. In particular, we consider statistical databases and the recently-proposed notion of differential privacy. Using the information-theoretic view, we discuss the bound that differential privacy induces on leakage, and the trade-off between utility and privacy.
This work has been partially supported by the project ANR-09-BLAN-0169-01 PANDA and by the INRIA DRI Equipe Associée PRINTEMPS. The work of Miguel E. Andrés has been supported by the LIX-Qualcomm postdoc fellowship.
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Alvim, M.S., Andrés, M.E., Chatzikokolakis, K., Palamidessi, C. (2011). Quantitative Information Flow and Applications to Differential Privacy. In: Aldini, A., Gorrieri, R. (eds) Foundations of Security Analysis and Design VI. FOSAD 2011. Lecture Notes in Computer Science, vol 6858. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23082-0_8
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