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Auditingf-differential privacy in one run

5 Pith papers cite this work. Polarity classification is still indexing.

5 Pith papers citing it

fields

cs.CR 3 cs.LG 2

years

2026 5

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representative citing papers

Privacy Auditing with Zero (0) Training Run

cs.CR · 2026-05-14 · unverdicted · novelty 8.0

Zero-Run auditing supplies valid lower bounds on differential privacy parameters from fixed member and non-member datasets by modeling and correcting distribution-shift confounding via causal-inference techniques.

Beyond Indistinguishability: Measuring Extraction Risk in LLM APIs

cs.CR · 2026-04-20 · unverdicted · novelty 7.0

Indistinguishability-based privacy is incomparable to extractability in LLMs, and a new (l, b)-inextractability definition with rank-based bounds provides a tighter measure of extraction risk than prior proxies.

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Showing 3 of 3 citing papers after filters.

  • Privacy Auditing with Zero (0) Training Run cs.CR · 2026-05-14 · unverdicted · none · ref 25

    Zero-Run auditing supplies valid lower bounds on differential privacy parameters from fixed member and non-member datasets by modeling and correcting distribution-shift confounding via causal-inference techniques.

  • Optimal Guarantees for Auditing R\'enyi Differentially Private Machine Learning cs.LG · 2026-05-21 · unverdicted · none · ref 28

    A hypothesis-testing framework with class-restricted Donsker-Varadhan estimators provides optimal non-asymptotic confidence intervals and minimax lower bounds for black-box auditing of Rényi DP guarantees.

  • Beyond Indistinguishability: Measuring Extraction Risk in LLM APIs cs.CR · 2026-04-20 · unverdicted · none · ref 16

    Indistinguishability-based privacy is incomparable to extractability in LLMs, and a new (l, b)-inextractability definition with rank-based bounds provides a tighter measure of extraction risk than prior proxies.