feat: add QMIAAttack quantile regression membership inference attack (#306)

[ssrhaso]

Summary

Implements the quantile regression membership inference attack from [Bertran et al., NeurIPS 2023] (https://arxiv.org/abs/2307.03694) - as described in #306.

Trains a single HistGradientBoostingRegressor at quantile level (1 − α) on non-member hinge scores to learn per-sample membership thresholds. A record is predicted as a member when its observed score exceeds the predicted threshold.

No shadow models required.

New files

• sacroml/attacks/qmia_attack.py - QMIAAttack class (18 tests, 100% coverage)
• sacroml/attacks/utils.py - hinge score, margin conversion, label helpers
• sacroml/attacks/report.py - create_qmia_report() with QMIA_INTRODUCTION
• sacroml/attacks/factory.py - registered as "qmia"
• tests/attacks/test_qmia_attack.py - 18 tests
• tests/attacks/test_factory.py - factory integration test
• tests/attacks/test_report.py - JSON sanitisation test
• examples/sklearn/benchmark_qmia_*.py - benchmark scripts
• CHANGELOG.md, README.md - updated

Incidental fixes

• attribute_attack.py: close matplotlib figures after saving (resource leak)
• target.py: skip serialising data arrays when dataset module is provided
• test_sklearn.py: fix LabelEncoder deprecation warning
• test_structural_attack.py: suppress ConvergenceWarning in MLP tests
• conftest.py: set matplotlib Agg backend, add session cleanup
• utils.py: refactor check_and_update_dataset to use dict lookup

Test results

• 157 passed, 0 failures
• qmia_attack.py: 100% line coverage

[codecov]

Codecov Report

❌ Patch coverage is 97.18310% with 4 lines in your changes missing coverage. Please review.
✅ Project coverage is 99.39%. Comparing base (a9524af) to head (891cff1).

Files with missing lines	Patch %	Lines
sacroml/attacks/utils.py	88.57%	4 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #435      +/-   ##
==========================================
- Coverage   99.51%   99.39%   -0.12%     
==========================================
  Files          23       24       +1     
  Lines        2687     2818     +131     
==========================================
+ Hits         2674     2801     +127     
- Misses         13       17       +4     

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[jim-smith]

I am not happy with the greatly relaxed bounds on the various attack metrics, for example going from:

assert metrics["TPR"] == pytest.approx(0.91, abs=0.01)
 assert metrics["FPR"] == pytest.approx(0.41, abs=0.01)

to

   assert 0.5 <= metrics["TPR"] <= 1.0
    assert 0.0 <= metrics["FPR"] <= 1.0

no longer checks that the algorithm is behaving reproducibly and the same way as previous versions,
because we are going from +/- 1% (which allows for minor changes in precision of floats etc on different platforms) to +/- 25%

I know this is partly to do with using randomly created data, but by specifying the random seeds it should still be possible to get reproducible behaviour and keep the acceptable difference in behaviour to within +/- 1% of whatever the new value is.

[shamykyzer]

Hello Jim,

Sorry for the oversight. I have tightened the QMIA test assertions to +/- 1% using pytest.approx, following the same pattern as test_factory. I have now replaced the loose bounds with exact expected values. I also added TPR@FPR threshold assertions because they test QMIA in detecting members at a controlled false positive rate. I think these are worth keeping, but let me know if you think otherwise.

I went more in-depth and just wanted to confirm my understanding, as I do not want to change anything outside of the QMIA scope.

Does test_factory checks correctness with tight bounds while other tests just verify the code runs and produces valid metrics? and wouldn't it have been better to add random_state to those fixtures and tighten them from the start?

test_lira_attack and test_worst_case_attack use loose assertions like assert 0 <= metrics["TPR"] <= 1 because their fixtures do not set random_state, so the metric values differ between runs.

Since test_factory runs the full end-to-end pipeline and is the only test with tight assertions because its get_target fixture sets random_state=1 making the results reproducible.

The QMIA fixtures do have random_state set, but I kept the loose format to stay consistent with the other individual attack tests. Should I tighten those as well, like I did in the factory test?

Please let me know if I have misunderstood anything.

Thanks!