opake-auditing

Empirical privacy auditing for Opake: one-run estimator (Steinke et al. 2023), coin-flip canary partitioning, and loss-based membership-inference attacks.

Install

Install the root package as described in the repository installation guide. Use its auditing extra to include this component.

opake-auditing depends on opake-engine and scipy; both install automatically with the extra.

Quick start

import opake.auditing as auditing
from opake.random import key

cf = auditing.coin_flip(dataset, num_canaries=1000, key=key(42))
# ... DP-SGD training loop ...
estimate = auditing.one_run(scores, coin_flip=cf)

By default, canaries are sampled from the dataset's natural rows. Pass candidate_indices= to restrict selection to a precommitted pool of constructed or selected records.

Layout

• opake.auditing.one_run — one-run estimator (Steinke et al. 2023)
• opake.auditing.coin_flip() — coin-flip canary partitioning
• opake.auditing.loss_scores() — loss-based membership inference via vmap