{"total":42,"rows":[{"id":"2112.03570","title":"Membership Inference Attacks From First Principles","created":"2021-12-07","cited_by_count":878,"area":"safety"},{"id":"2209.02299","title":"A Survey of Machine Unlearning","created":"2022-09-06","cited_by_count":312,"area":"safety"},{"id":"2203.03929","title":"Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks","created":"2022-03-08","cited_by_count":175,"area":"safety"},{"id":"2403.03329","title":"Guardrail Baselines for Unlearning in LLMs","created":"2024-03-05","cited_by_count":96,"area":"safety"},{"id":"2102.02551","title":"ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models","created":"2021-02-04","cited_by_count":85,"area":"safety"},{"id":"2110.08324","title":"Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture","created":"2021-10-15","cited_by_count":67,"area":"safety"},{"id":"2106.06916","title":"Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization","created":"2021-06-13","cited_by_count":52,"area":"safety"},{"id":"2404.03233","title":"Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning","created":"2024-04-04","cited_by_count":48,"area":"safety"},{"id":"2102.04140","title":"Quantifying and Mitigating Privacy Risks of Contrastive Learning","created":"2021-02-08","cited_by_count":36,"area":"safety"},{"id":"2405.07406","title":"Machine Unlearning: A Comprehensive Survey","created":"2024-05-13","cited_by_count":35,"area":"safety"},{"id":"2111.07608","title":"Property Inference Attacks Against GANs","created":"2021-11-15","cited_by_count":33,"area":"safety"},{"id":"2405.20272","title":"Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable","created":"2024-05-30","cited_by_count":25,"area":"safety"},{"id":"2202.03460","title":"Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning","created":"2022-02-07","cited_by_count":24,"area":"safety"},{"id":"2309.00886","title":"Tight Bounds for Machine Unlearning via Differential Privacy","created":"2023-09-02","cited_by_count":18,"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":"2408.05131","title":"Range Membership Inference Attacks","created":"2024-08-09","cited_by_count":12,"area":"safety"},{"id":"2104.08273","title":"Membership Inference Attacks on Knowledge Graphs","created":"2021-04-16","cited_by_count":11,"area":"safety"},{"id":"2405.19211","title":"Gone but Not Forgotten: Improved Benchmarks for Machine Unlearning","created":"2024-05-29","cited_by_count":9,"area":"safety"},{"id":"2205.06369","title":"How to Combine Membership-Inference Attacks on Multiple Updated Models","created":"2022-05-12","cited_by_count":8,"area":"safety"},{"id":"2502.19770","title":"TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning","created":"2025-02-27","cited_by_count":6,"area":"safety"},{"id":"2505.08576","title":"MUBox: A Critical Evaluation Framework of Deep Machine Unlearning","created":"2025-05-13","cited_by_count":6,"area":"safety"},{"id":"2205.06469","title":"l-Leaks: Membership Inference Attacks with Logits","created":"2022-05-13","cited_by_count":5,"area":"safety"},{"id":"2409.03741","title":"Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?","created":"2024-09-05","cited_by_count":5,"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":"2510.13451","title":"Toward Efficient Inference Attacks: Shadow Model Sharing via Mixture-of-Experts","created":"2025-10-15","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":"2307.06123","title":"SoK: Comparing Different Membership Inference Attacks with a Comprehensive Benchmark","created":"2023-07-12","cited_by_count":3,"area":"safety"},{"id":"2308.00553","title":"FLAIRS: FPGA-Accelerated Inference-Resistant & Secure Federated Learning","created":"2023-08-01","cited_by_count":3,"area":"safety"},{"id":"2502.09396","title":"A hierarchical approach for assessing the vulnerability of tree-based classification models to membership inference attack","created":"2025-02-13","cited_by_count":3,"area":"safety"},{"id":"2503.00062","title":"CRFU: Compressive Representation Forgetting Against Privacy Leakage on Machine Unlearning","created":"2025-02-27","cited_by_count":3,"area":"safety"},{"id":"2211.02245","title":"Unintended Memorization and Timing Attacks in Named Entity Recognition Models","created":"2022-11-04","cited_by_count":2,"area":"safety"},{"id":"2306.03600","title":"Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations","created":"2023-06-06","cited_by_count":1,"area":"safety"},{"id":"2512.00272","title":"WARP: Weight Teleportation for Attack-Resilient Unlearning Protocols","created":"2025-11-29","cited_by_count":1,"area":"safety"},{"id":"2601.07214","title":"BlindU: Blind Machine Unlearning without Revealing Erasing Data","created":"2026-01-12","cited_by_count":1,"area":"safety"},{"id":"2602.16596","title":"Sequential Membership Inference Attacks","created":"2026-02-18","cited_by_count":1,"area":"safety"},{"id":"2209.11020","title":"Privacy Attacks Against Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models","created":"2022-09-22","cited_by_count":0,"area":"safety"},{"id":"2412.07575","title":"Defending Against Neural Network Model Inversion Attacks via Data Poisoning","created":"2024-12-10","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":"2503.09365","title":"Membership Inference Attacks fueled by Few-Short Learning to detect privacy leakage tackling data integrity","created":"2025-03-12","cited_by_count":0,"area":"safety"},{"id":"2504.21042","title":"What's Pulling the Strings? Evaluating Integrity and Attribution in AI Training and Inference through Concept Shift","created":"2025-04-28","cited_by_count":0,"area":"safety"},{"id":"2506.01777","title":"DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems","created":"2025-06-02","cited_by_count":0,"area":"safety"},{"id":"2509.16525","title":"CAF\\'E: Causal Black-Box Testing of Machine Unlearning","created":"2025-09-20","cited_by_count":0,"area":"safety"}],"next_cursor":null}