Source code for opacus.accountants.gdp

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import warnings

from .accountant import IAccountant
from .analysis import gdp as privacy_analysis


[docs]class GaussianAccountant(IAccountant): def __init__(self): warnings.warn( "GDP accounting is experimental and can underestimate privacy expenditure." "Proceed with caution. More details: https://arxiv.org/pdf/2106.02848.pdf" ) super().__init__()
[docs] def step(self, *, noise_multiplier: float, sample_rate: float): if len(self.history) >= 1: last_noise_multiplier, last_sample_rate, num_steps = self.history.pop() if ( last_noise_multiplier != noise_multiplier or last_sample_rate != sample_rate ): raise ValueError( "Noise multiplier and sample rate have to stay constant in GaussianAccountant." ) else: self.history = [ (last_noise_multiplier, last_sample_rate, num_steps + 1) ] else: self.history = [(noise_multiplier, sample_rate, 1)]
[docs] def get_epsilon(self, delta: float, poisson: bool = True) -> float: """ Return privacy budget (epsilon) expended so far. Args: delta: target delta poisson: ``True`` is input batches was sampled via Poisson sampling, ``False`` otherwise """ compute_eps = ( privacy_analysis.compute_eps_poisson if poisson else privacy_analysis.compute_eps_uniform ) noise_multiplier, sample_rate, num_steps = self.history[-1] return compute_eps( steps=num_steps, noise_multiplier=noise_multiplier, sample_rate=sample_rate, delta=delta, )
def __len__(self): return len(self.history)
[docs] @classmethod def mechanism(cls) -> str: return "gdp"