Qiflib: A Python package for Quantitative Information Flow (QIF)

Updated on Jul 2022

QIFLib is a Python library for modeling, analyzing, and quantifying information leakage in probabilistic systems using the Quantitative Information Flow (QIF) framework.
It provides a clean, composable set of primitives — secrets, channels, hyper-distributions, vulnerability and uncertainty functions — so you can build end-to-end analyses of how much information an adversary could learn.

For more details see the book The Science of Quantitative Information Flow.

Documentation

The documentation is available at https://pypi.org/project/qiflib/.

Features

Secrets Modeling — define hidden states with flexible prior distributions.
Channels — represent probabilistic mappings $p(y\mid x)$ from secrets to observations.
Hyper-distributions — automatically compute joint $p(x,y)$, outer $p(y)$, and inners $p(x\mid y)$.
Vulnerability Analysis — use $g$-vulnerability to quantify an adversary’s expected gain before and after observing outputs.
Uncertainty Analysis — use $\ell$-uncertainty to measure the expected loss under optimal strategies.
Leakage Metrics — compute additive and multiplicative leakage.
Flexible Functions — provide gain/loss as matrices or Python callables.

Why QIF?

Traditional security analysis asks whether a secret can be inferred. QIF quantifies how much an adversary is expected to learn, under probabilistic models of system behavior and strategies.
Useful for:

privacy-preserving data publishing,
side-channel analysis,
communication protocol evaluation,
decision-making under uncertainty.

Installation

You can install via PyPI:

pip install qiflib

or manuallly by copying this repository to your local machine and running:

pip install path/to/qiflib

To verify if the package was installed correctly, you can run tests:

cd path/to/qiflib
python -m unittest discover tests

Example

from qiflib.core.secrets import Secrets
from qiflib.core.channel import Channel
from qiflib.core.hyper import Hyper
from qiflib.core.gvulnerability import GVulnerability

# Secrets & prior
secrets = Secrets(["x0", "x1"], [0.6, 0.4])

# Channel matrix: rows sum to 1
outputs = ["y0", "y1"]
C = [
      [0.8, 0.2],  # p(y|x0)
      [0.1, 0.9],  # p(y|x1)
]
channel = Channel(secrets, outputs, C)

# Hyper-distribution
hyper = Hyper(channel)

# Gain function (1 if correct guess, else 0)
actions = ["guess_x0", "guess_x1"]
def gfun(w, x): return 1.0 if w == x else 0.0

g = GVulnerability(secrets, actions, gfun)

print("Prior vulnerability:", g.prior_vulnerability())
print("Posterior vulnerability:", g.posterior_vulnerability(hyper))

License

This project is licensed under the MIT License.