Recoverable Identifier

arXiv:2605.06340 · detector doi_compliance · incontrovertible · 2026-05-19 12:46:19.552624+00:00

Paper page Integrity report arXiv Try DOI

Evidence text

The DSA Transparency Database: Auditing self-reported moderation actions by social media. Proceedings of the ACM on Human-Computer Interaction , 9(2), pp. 1–28. A vailable at: https://doi.org/10. 1145/3711085. Yang, Y.-T., Zhang, T., and Zhu, Q. (2025) Herd accountability of privacy-preserving algorithms: A Stackelberg game approach. IEEE Transactions on Information Foren- sics and Security , 20, pp. 2237–2251. A vailable at: https://doi.org/10.1109/tifs.2025. 3540357. A Implementation Details A.1 Reproduction commands To reproduce Table 2 on a fresh environment: uv venv && uv pip install -e . uv run python -m audit_gaming_benchmark.run \ --config experiments/configs/default.yaml --out results/default uv run python -m audit_gaming_benchmark.run \ --config experiments/configs/attrition.yaml --out results/attrition Each run writes a single sweep.json with the per-cell results. New strategies and policies are added by implementing the respective Auditee / Auditor protocol and reg- istering with the runner. 15 A.2 Code structure The benchmark is a single Python package ( audit_gaming_benchmark) with five source modules: • env.py: AuditEnv (the 𝑇-round game), Trajectory (per-round outcomes con- tainer), and Auditor / Auditee structural Protocols. • strategies.py: HonestAuditee, HonestNoisyAuditee, DelayStrategy, DriftStrategy, CherryPickStrategy, AttritionStrategy, OffAuditDriftStrategy. • policies.py: OneShotPolicy, PeriodicPolicy, SurprisePolicy (the implemen- tation listing for

Evidence payload

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  "reconstructed_doi": "10.1109/tifs.2025.3540357.A",
  "ref_index": 15,
  "resolved_title": null,
  "verdict_class": "incontrovertible"
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