leakiEst

leakiEst — Tool for "Leaking information Estimation"

leakiEst is a command-line tool and Java API for estimating information leakage from previous records of a system's execution.

  • It performs statistical tests on either discrete or continuous data to distinguish an insecure system with a very small information leak from a secure one with no leakage, and calculates confidence intervals for its estimations.
  • It can estimate the most popular measures of information leakage: mutual information and min-entropy leakage.

leakiEst is freely available, and can be downloaded from the download page.

News

  • 7 February 2019: leakiEst Version 1.4.9 is released.
    In this version a minor bug in zero leakage test with -co option is fixed and an option -testco is added.
  • 29 August 2018: leakiEst Version 1.4.8 is released.
    In this version bugs in printing the results (for ARFF files) and in printing the prior uncertainty are fixed.
  • 3 March 2018: leakiEst Version 1.4.7 is released.
    In this version bugs in the statistical estimation of continuous mutual informations are fixed.
  • 20 January 2015: leakiEst Version 1.4.5 is released.
    In this version some bugs in the statistical estimation of mutual informations are fixed.
  • 12 August 2014: leakiEst Version 1.4.2 is released.
    In this version some bugs in the statistical estimation of min-entropy leakage using Chi-square tests are fixed.
  • 25 April 2014: leakiEst Version 1.4.0 is released.
    In this version the statistical estimation of min-entropy leakage using Chi-square tests is supported.
    For observations files and ARFF files, input distributions are also estimated from the trial runs.
  • See History for old releases.

People

The authors of the underlying theory and implementation of leakiEst are:

Bug reports, feature requests and suggestions should be sent to the maintainer, Yusuke Kawamoto.

Publications

For a list of related publications and other documents by the authors, see the information leakage publications page.

Acknowledgement

Some of the work was supported by EPSRC Research Grant EP/J009075/1. Work by Yusuke Kawamoto was partially supported by a postdoc grant funded by the IDEX Digital Society project in France.