2.1 Quantifying Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2.2 Entropy and Mutual Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2.1 Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2.2 Mutual Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2.3 Example: Test...

One of the most important properties of a cryptographic system is a proof of its security. In the present work, information-theoretic methods are used for proving the security of unconditionally secure cryptosystems. The security of such systems does not depend on unproven intractability assumptions. A survey of entropy measures and their applications in cryptography is presented. A new informa...

In this work we demonstrate various weaknesses of the random number generator (RNG) in the OpenSSL cryptographic library. We show how OpenSSL’s RNG, knowingly in a low entropy state, potentially leaks low entropy secrets in its output, which were never intentionally fed to the RNG by client code, thus posing vulnerabilities even when in the given usage scenario the low entropy state is respecte...

The Kolmogorov-Sinai entropy is a far reaching dynamical generalization of Shannon entropy of information systems. This entropy works perfectly for probability measure preserving (p.m.p.) transformations. However, it is not useful when there is no finite invariant measure. There are certain successful extensions of the notion of entropy to infinite measure spaces, or transformations with ...

Delft University of Technology Faculty of Electrical Engineering, Mathematics and Computer Science CE-MS-2003-13 Entropy Decoding is an essentially sequential task. Executing this task on a processor that benefits from Instruction Level Parallelism (ILP), Data Level Parallelism (DLP) or both requires an efficient implementation of Entropy Decoding. Entropy Decoding forms the part of MPEG-2 Deco...

Projective measurement can increase the entropy of a state ρ, the increased entropy is not only up to the basis of projective measurement, but also has something to do with the properties of the state itself. In this paper we define this increased entropy as basis entropy. And then we discuss the usefulness of this new concept by showing its application in explaining the success probability of ...

Centered numerical fluxes can be constructed for compressible Euler equations which preserve kinetic energy in the semi-discrete finite volume scheme. The essential feature is that the momentum flux should be of the form f m j+ 1 2 = p̃j+ 1 2 +uj+ 1 2 f ρ j+ 2 where uj+ 2 = (uj+uj+1)/2 and p̃j+ 1 2 , f ρ j+ 1 2 are any consistent approximations to the pressure and the mass flux. This scheme thus ...