Diffprivlib 0.5.0

This release of diffprivlib features a number of additions, fixes and enhancements. This version of diffprivlib supports Python version 3.7 to 3.9.

Added

• A differentially private random forest classifier, RandomForestClassifier, has been added to the models module (#41, by mismayil)
• The Exponential and PermuteAndFlip mechanisms now support monotonic utility functions as an input parameter (08c4d98)
• Templates have been added for issues and pull requests (#52, by stefano81)
• We are transitioning to Github Actions for tests and other checks (#54)

Changed

Breaking:

• Mirroring the equivalent behaviour in scikit-learn (since version 0.23), most parameters for diffprivlib models must now be specified as keyword arguments. Failing to do so will raise a TypeError. (5fa781e)

Non-breaking:

• The Laplace, Gaussian, Vector and Bingham distributions are now securely sampled to prevent privacy leakage due to their floating point representation (#47)
• Diffprivlib now requires scikit-learn 0.23, numpy 1.19, and scipy 1.5, or later (#51)

Fixed

• Bug in quantile for uniform data fixed (#43, d5ac459)
• Optimised upper bound for var (#45, by justanotherlad)
• Bug in Exponential mechanism (#50, by justanotherlad)
• histogramdd and histogram2d now prioritise bin edges from bins over range. (96ef33c)

Removed

• The Wishart mechanism has been removed (#42, eeae0d7)