dp-learning-ff 0.0.7

Repository of differentially-private methods for learning from features.

Description

Repository of differentially-private methods for learning from features.

Usage

Installation

pip install dp-learning-ff

Private Prototype Calculation

1. Set your total privacy budget in (0,rho)-zCDP.
2. Divide your total budget into a budget per step. Good starting values are listed below. The algorithm will perform len(Ps) many steps.

Ps = [rho] # One iteration
Ps = [5 / 64 * rho, 7 / 64 * rho, 52 / 64 * rho] # Three iterations

3. Call the private prototype calculation using your train_preds, train_targets and Ps as outlined above.

num_samples = 100
dimensionality = 10
num_classes = 5
train_preds = np.random.normal(0,1,(num_samples, dimensionality))
train_targets = np.random.randint(num_classes, size=(num_samples)) # Supports unbalanced classes
private_prototypes = dp_learning_ff.give_private_prototypes(train_preds, train_targets, Ps)
>>> private_prototypes.shape
(5, 10)
>>> private_prototypes
array([[-1.08239659,  0.50517265,  0.46295668,  0.11665974, -0.41539363,
         0.46428818, -0.30204127, -0.20684517, -0.11748276, -0.44173581],
       [-0.97640077,  0.73495051, -0.3125302 ,  0.39733846, -0.15187237,
        -0.09618042,  0.50008494,  0.06408969, -0.59472233,  0.56000984],
       [-0.07384877, -0.04661516,  0.83469973, -0.18854654, -0.3631222 ,
        -0.56158617,  0.03480437, -0.17749153, -0.25144926,  0.99669163],
       [ 0.02682651, -0.57948712, -0.33462215,  0.32657359, -0.13894844,
        -1.16420508,  0.84630674, -0.0343235 , -0.38193481,  0.29124748],
       [ 1.12158357,  0.17188541, -0.2582887 , -0.2140504 ,  0.06759121,
        -0.26975582, -0.46858189,  0.67295234,  0.64183255, -0.10445519]])

Acknowledgements

Prototype Calculations use the coinpress algorithm from the paper CoinPress: Practical Private Mean and Covariance Estimation, authored by Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan Ullman. Code contributed by all four authors.

@incollection{BiswasDKU20,
  title         = {CoinPress: Practical Private Mean and Covariance Estimation},
  author        = {Biswas, Sourav and Dong, Yihe and Kamath, Gautam and Ullman, Jonathan},
  booktitle = {Advances in Neural Information Processing Systems 33},
  url       = {arXiv preprint arXiv:2006.06618},
  year          = {2020}
}