{"total":67,"rows":[{"id":"2402.07841","title":"Do Membership Inference Attacks Work on Large Language Models?","created":"2024-02-12","cited_by_count":195,"area":"safety"},{"id":"2312.03262","title":"Low-Cost High-Power Membership Inference Attacks","created":"2023-12-06","cited_by_count":107,"area":"safety"},{"id":"2212.10986","title":"SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning","created":"2022-12-21","cited_by_count":51,"area":null},{"id":"2404.17399","title":"Evaluations of Machine Learning Privacy Defenses are Misleading","created":"2024-04-26","cited_by_count":36,"area":null},{"id":"2303.03446","title":"Students Parrot Their Teachers: Membership Inference on Model Distillation","created":"2023-03-06","cited_by_count":33,"area":"safety"},{"id":"2411.10512","title":"On the Privacy Risk of In-context Learning","created":"2024-11-15","cited_by_count":32,"area":"safety"},{"id":"2410.22235","title":"Auditing $f$-Differential Privacy in One Run","created":"2024-10-29","cited_by_count":30,"area":null},{"id":"2307.01610","title":"Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Prediction","created":"2023-07-04","cited_by_count":29,"area":"safety"},{"id":"2212.00612","title":"Purifier: Defending Data Inference Attacks via Transforming Confidence Scores","created":"2022-12-01","cited_by_count":15,"area":"safety"},{"id":"2502.19726","title":"Tokens for Learning, Tokens for Unlearning: Mitigating Membership Inference Attacks in Large Language Models via Dual-Purpose Training","created":"2025-02-27","cited_by_count":15,"area":"safety"},{"id":"2311.00919","title":"MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training","created":"2023-11-02","cited_by_count":12,"area":"safety"},{"id":"2309.03081","title":"ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning","created":"2023-09-06","cited_by_count":11,"area":"offlinerl"},{"id":"2404.02462","title":"A Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability","created":"2024-04-03","cited_by_count":9,"area":"safety"},{"id":"2506.13009","title":"Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective","created":"2025-06-16","cited_by_count":9,"area":"safety"},{"id":"2407.01919","title":"A Method to Facilitate Membership Inference Attacks in Deep Learning Models","created":"2024-07-02","cited_by_count":7,"area":"safety"},{"id":"2407.07926","title":"Synthetic Data: Revisiting the Privacy-Utility Trade-off","created":"2024-07-09","cited_by_count":7,"area":null},{"id":"2508.09603","title":"The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage","created":"2025-08-13","cited_by_count":7,"area":"safety"},{"id":"2111.01363","title":"Knowledge Cross-Distillation for Membership Privacy","created":"2021-11-02","cited_by_count":6,"area":"safety"},{"id":"2203.00915","title":"MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members","created":"2022-03-02","cited_by_count":6,"area":"safety"},{"id":"2402.05453","title":"Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss","created":"2024-02-08","cited_by_count":6,"area":"safety"},{"id":"2501.00418","title":"Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models","created":"2024-12-31","cited_by_count":6,"area":null},{"id":"2506.17353","title":"Differentiation-Based Extraction of Proprietary Data from Fine-Tuned LLMs","created":"2025-06-20","cited_by_count":6,"area":"alignment"},{"id":"2202.02242","title":"Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute Inference Attacks","created":"2022-02-04","cited_by_count":5,"area":null},{"id":"2205.09986","title":"SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning","created":"2022-05-20","cited_by_count":5,"area":null},{"id":"2502.10450","title":"Trustworthy AI: Safety, Bias, and Privacy -- A Survey","created":"2025-02-11","cited_by_count":5,"area":"safety"},{"id":"2312.04692","title":"Diffence: Fencing Membership Privacy With Diffusion Models","created":"2023-12-07","cited_by_count":4,"area":"diffusion"},{"id":"2401.00996","title":"Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software Deployment","created":"2024-01-02","cited_by_count":4,"area":"safety"},{"id":"2404.17674","title":"Center-Based Relaxed Learning Against Membership Inference Attacks","created":"2024-04-26","cited_by_count":4,"area":"safety"},{"id":"2409.06280","title":"Anonymity Unveiled: A Practical Framework for Auditing Data Use in Deep Learning Models","created":"2024-09-10","cited_by_count":4,"area":null},{"id":"2410.06814","title":"Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning","created":"2024-10-09","cited_by_count":4,"area":"safety"},{"id":"2206.05565","title":"NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks","created":"2022-06-11","cited_by_count":3,"area":"safety"},{"id":"2407.16164","title":"Representation Magnitude has a Liability to Privacy Vulnerability","created":"2024-07-23","cited_by_count":3,"area":null},{"id":"2409.15868","title":"Privacy Evaluation Benchmarks for NLP Models","created":"2024-09-24","cited_by_count":3,"area":null},{"id":"2410.16657","title":"Dual-Model Defense: Safeguarding Diffusion Models from Membership Inference Attacks through Disjoint Data Splitting","created":"2024-10-22","cited_by_count":3,"area":"diffusion"},{"id":"2507.16872","title":"CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage","created":"2025-07-22","cited_by_count":3,"area":null},{"id":"2508.07054","title":"Membership and Memorization in LLM Knowledge Distillation","created":"2025-08-09","cited_by_count":3,"area":"distill"},{"id":"2508.19641","title":"Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses","created":"2025-08-27","cited_by_count":3,"area":null},{"id":"2310.00488","title":"Membership Privacy Risks of Sharpness Aware Minimization","created":"2023-09-30","cited_by_count":2,"area":null},{"id":"2401.04929","title":"Learning-Based Difficulty Calibration for Enhanced Membership Inference Attacks","created":"2024-01-10","cited_by_count":2,"area":"safety"},{"id":"2511.16792","title":"Membership Inference Attacks Beyond Overfitting","created":"2025-11-20","cited_by_count":2,"area":"safety"},{"id":"2601.15394","title":"Memorization Dynamics in Knowledge Distillation for Language Models","created":"2026-01-21","cited_by_count":2,"area":"distill"},{"id":"2205.08821","title":"Lessons Learned: Defending Against Property Inference Attacks","created":"2022-05-18","cited_by_count":1,"area":"safety"},{"id":"2210.01864","title":"Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints","created":"2022-10-04","cited_by_count":1,"area":null},{"id":"2310.12112","title":"A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses","created":"2023-10-18","cited_by_count":1,"area":null},{"id":"2402.01114","title":"Double-Dip: Thwarting Label-Only Membership Inference Attacks with Transfer Learning and Randomization","created":"2024-02-02","cited_by_count":1,"area":"safety"},{"id":"2404.07139","title":"Towards a Game-theoretic Understanding of Explanation-based Membership Inference Attacks","created":"2024-04-10","cited_by_count":1,"area":"safety"},{"id":"2505.10351","title":"A Unified and Scalable Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability","created":"2025-05-15","cited_by_count":1,"area":"safety"},{"id":"2505.11837","title":"On Membership Inference Attacks in Knowledge Distillation","created":"2025-05-17","cited_by_count":1,"area":"safety"},{"id":"2505.13362","title":"Dynamic Probabilistic Noise Injection for Membership Inference Defense","created":"2025-05-19","cited_by_count":1,"area":"safety"},{"id":"2506.19360","title":"SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation","created":"2025-06-24","cited_by_count":1,"area":null},{"id":"2512.15335","title":"Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference","created":"2025-12-17","cited_by_count":1,"area":"safety"},{"id":"2512.20821","title":"Divided We Fall: Defending Against Adversarial Attacks via Soft-Gated Fractional Mixture-of-Experts with Randomized Adversarial Training","created":"2025-12-23","cited_by_count":1,"area":"moe"},{"id":"2601.06866","title":"United We Defend: Collaborative Membership Inference Defenses in Federated Learning","created":"2026-01-11","cited_by_count":1,"area":"safety"},{"id":"2601.15874","title":"SoK: Challenges in Tabular Membership Inference Attacks","created":"2026-01-22","cited_by_count":1,"area":"safety"},{"id":"2602.02296","title":"Decoupling Generalizability and Membership Privacy Risks in Neural Networks","created":"2026-02-02","cited_by_count":1,"area":null},{"id":"2603.24857","title":"AI Security in the Foundation Model Era: A Comprehensive Survey from a Unified Perspective","created":"2026-03-25","cited_by_count":1,"area":"safety"},{"id":"2203.05212","title":"Membership Privacy Protection for Image Translation Models via Adversarial Knowledge Distillation","created":"2022-03-10","cited_by_count":0,"area":"safety"},{"id":"2402.00342","title":"Survey of Privacy Threats and Countermeasures in Federated Learning","created":"2024-02-01","cited_by_count":0,"area":null},{"id":"2402.12189","title":"Amplifying Training Data Exposure through Fine-Tuning with Pseudo-Labeled Memberships","created":"2024-02-19","cited_by_count":0,"area":"safety"},{"id":"2406.07917","title":"Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks","created":"2024-06-12","cited_by_count":0,"area":"safety"},{"id":"2502.08001","title":"Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation","created":"2025-02-11","cited_by_count":0,"area":"safety"},{"id":"2512.08291","title":"Exposing and Defending Membership Leakage in Vulnerability Prediction Models","created":"2025-12-09","cited_by_count":0,"area":"safety"},{"id":"2512.15468","title":"How Do Semantically Equivalent Code Transformations Impact Membership Inference on LLMs for Code?","created":"2025-12-17","cited_by_count":0,"area":"safety"},{"id":"2601.18909","title":"How Is Uncertainty Propagated in Knowledge Distillation?","created":"2026-01-26","cited_by_count":0,"area":"distill"},{"id":"2603.13186","title":"Learnability and Privacy Vulnerability are Entangled in a Few Critical Weights","created":"2026-03-13","cited_by_count":0,"area":null},{"id":"2605.12264","title":"Reconstruction of Personally Identifiable Information from Proprietary Data in Supervised Fine-Tuned Models","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2610.18486","title":"SoK: Rethinking Membership Inference in Tabular Machine Learning","created":"2026-10-13","cited_by_count":0,"area":"safety"}],"next_cursor":null}