QIF_LIB

A Quantitative Information Flow Open Source Library

Software present in the lib

This section is organized by modules.

1.1: Given a vector of numbers, verify whether it constitutes a valid probability distribution.
1.2: Given a matrix of number, verify whether it constitutes a valid channel matrix.
1.3: Print beautifully a prior on the screen.
1.4: Print beautifully a channel matrix on the screen.

2.1: Given a prior and a channel matrix, compute the corresponding joint probability distribution.
2.2: Given a prior and a channel matrix, compute the corresponding hyper-distribution (i.e., the set of posterior distributions and the outer distribution on them).

3.1: Given a prior distribution, compute its Shannon entropy.
3.2: Given a prior distribution, compute its Guessing entropy.
3.3: Given a prior distribution, compute its Bayes vulnerability.
3.4: Given a prior distribution and a value n>=1, compute the probability of guessing correctly within n tries. (Note that when n = 1 this function coincides with Bayes vulnerability).
3.5: Given a prior distribution and a gain function, compute the g-vulnerability. (Note that when the g-function is gid, you recover Bayes vulnerability).

4.1: Create functions that, given a prior and a channel matrix, compute the corresponding posterior information measures as in the items (a)-(e) of item (3) above.
4.1.1: Compute its Shannon entropy.
4.1.2: Compute its Guessing entropy.
4.1.3: Compute its Bayes vulnerability.
4.1.4: Given a posterior distribution and a value n>=1, compute the probability of guessing correctly within n tries.
4.1.5: Given a posterior distribution and a gain function, compute the g-vulnerability.

5.1: Given a prior, a channel matrix, and a gain function, compute the corresponding additive leakage.
5.2: Given a prior, a channel matrix, and a gain function, compute the corresponding multiplicative leakage.