privacy auditing

Fast-MIA: Efficient and Scalable Membership Inference for LLMs ACL 2026 Neural Breadcrumbs: Membership Inference Attacks on LLMs Through Hidden State and Attention Pattern Analysis EACL 2026 Powerful Training-Free Membership Inference Against Fine-Tuned Autoregressive Language Models ACL 2026 Privacy Auditing of Multi-Domain Graph Pre-Trained Model Under Membership Inference Attacks AAAI 2026 Privacy Auditing of Large Language Models ICLR 2025 The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD ICLR 2025 Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model ICLR 2025 Auditing Privacy Mechanisms via Label Inference Attacks NeurIPS 2024 Confidential-DPproof: Confidential Proof of Differentially Private Training ICLR 2024 Nearly Tight Black-Box Auditing of Differentially Private Machine Learning NeurIPS 2024 Noisy Neighbors: Efficient membership inference attacks against LLMs ACL 2024 One-shot Empirical Privacy Estimation for Federated Learning ICLR 2024 PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining NeurIPS 2024 Quantifying Unintended Memorization in BEST-RQ ASR Encoders Interspeech 2024 Privacy Auditing with One (1) Training Run NeurIPS 2023 Unleashing the Power of Randomization in Auditing Differentially Private ML NeurIPS 2023 A General Framework for Auditing Differentially Private Machine Learning NeurIPS 2022 Auditing Differentially Private Machine Learning: How Private is Private SGD? NeurIPS 2020 On Log-Loss Scores and (No) Privacy EMNLP 2020 Tamper-Proof Privacy Auditing for Artificial Intelligence Systems IJCAI 2018