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v0.1.0 - first release

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@BAder82t BAder82t released this 27 Apr 15:54
· 7 commits to main since this release
v0.1.0
2ef61b1

First release of `fairlearn-fhe` — drop-in encrypted Fairlearn metrics
over CKKS via TenSEAL (default) or OpenFHE (opt-in).

Highlights

  • 12 canonical Fairlearn metrics ported to CKKS:
    `selection_rate`, `true_positive_rate`, `true_negative_rate`,
    `false_positive_rate`, `false_negative_rate`, `mean_prediction`,
    `demographic_parity_difference`, `demographic_parity_ratio`,
    `equalized_odds_difference`, `equalized_odds_ratio`,
    `equal_opportunity_difference`, `equal_opportunity_ratio`.
  • `MetricFrame`, `EncryptedMetricFrame`, and
    `make_derived_metric` mirroring the Fairlearn API surface.
  • TenSEAL and OpenFHE backends behind a single `build_context`
    dispatch.
  • Two trust models: encrypted `y_pred` with plaintext sensitive
    features (Mode A, depth 1), or fully-encrypted `y_pred` and
    sensitive features (Mode B, depth 2).
  • `MetricEnvelope` captures parameter-set hash, observed depth,
    and op counts. `validate_envelope()` provides dependency-light
    audit-envelope verification.
  • `fairlearn-fhe-verify` CLI for regulator-side envelope checks
    without importing an FHE backend.
  • Optional Ed25519 envelope signing (`pip install
    'fairlearn-fhe[signing]'`) plus signature verification.
  • Replay metadata records metric kwargs, trust model, and input
    hashes so audits are reproducible.
  • `audit_metric()` one-call wrapper produces an audit envelope.

Numerical fidelity

Default settings yield `< 1e-4` absolute error vs the plaintext
Fairlearn baseline. The included benchmark
(`benchmarks/bench_metrics.py`, n=1024, 3 sensitive groups, depth-6
circuit) gives:

backend dp_diff dp abs err eo_diff eo abs err
tenseal 284 ms 1e-7 562 ms 2e-7
openfhe 505 ms 2e-10 1015 ms 4e-11

OpenFHE wins on numeric error; TenSEAL wins on speed and ships via
`pip` on every supported platform.

Install

```bash
pip install fairlearn-fhe # tenseal backend (default)
pip install 'fairlearn-fhe[openfhe]' # add openfhe backend (requires C++ build)
pip install 'fairlearn-fhe[signing]' # add Ed25519 envelope signing helpers
```

Quality

  • 102 tests, 87% line coverage, ruff clean.
  • Tests cover edge cases, envelope validation, backend dispatch,
    encrypted arithmetic, CLI verification, and optional signature
    verification.

Full changelog: see docs/changelog.md.

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