Privacy Random Variable (PRV) Accountant

A fast algorithm to optimally compose privacy guarantees of differentially private (DP) algorithms to arbitrary accuracy. Our method is based on the notion of privacy loss random variables to quantify the privacy loss of DP algorithms. For more details see [1].

Installation

pip install git+https://github.com/microsoft/prv_accountant.git

Examples

Getting epsilon estimate directly from the command line.

compute-dp-epsilon --sampling-probability 5e-3 --noise-multiplier 0.8 --delta 1e-6 --num-compositions 1000

Or, use it in python code

from prv_accountant import Accountant

accountant = Accountant(
	noise_multiplier=0.8,
	sampling_probability=5e-3
	delta=1e-6,
	eps_error=0.1,
	max_compositions=1000
)

eps_low, eps_estimate, eps_upper = accountant.compute_epsilon(num_compositions=1000)

For more examples, have a look in the notebooks directory.

References

[1] Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz. Numerical Composition of Differential Privacy. arXiv. Preprint posted online June 5, 2021. arXiv

Contributing

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