{"total":35,"rows":[{"id":"2402.00888","title":"Security and Privacy Challenges of Large Language Models: A Survey","created":"2024-01-30","cited_by_count":186,"area":"safety"},{"id":"2208.12268","title":"FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning","created":"2022-08-25","cited_by_count":91,"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":"2302.07225","title":"Bounding Training Data Reconstruction in DP-SGD","created":"2023-02-14","cited_by_count":46,"area":"safety"},{"id":"2403.13682","title":"Threats, Attacks, and Defenses in Machine Unlearning: A Survey","created":"2024-03-20","cited_by_count":39,"area":"safety"},{"id":"2401.05146","title":"Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics","created":"2024-01-10","cited_by_count":27,"area":"safety"},{"id":"2210.16424","title":"Machine Unlearning of Federated Clusters","created":"2022-10-28","cited_by_count":22,"area":"safety"},{"id":"2206.03317","title":"Subject Membership Inference Attacks in Federated Learning","created":"2022-06-07","cited_by_count":20,"area":"safety"},{"id":"2502.12176","title":"Ten Challenging Problems in Federated Foundation Models","created":"2025-02-14","cited_by_count":20,"area":"safety"},{"id":"2302.12685","title":"Active Membership Inference Attack under Local Differential Privacy in Federated Learning","created":"2023-02-24","cited_by_count":17,"area":"safety"},{"id":"2111.00430","title":"Efficient passive membership inference attack in federated learning","created":"2021-10-31","cited_by_count":16,"area":"safety"},{"id":"2403.02116","title":"Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks","created":"2024-03-04","cited_by_count":13,"area":"safety"},{"id":"2306.01356","title":"FedCIP: Federated Client Intellectual Property Protection with Traitor Tracking","created":"2023-06-02","cited_by_count":12,"area":"safety"},{"id":"2402.06289","title":"FedMIA: An Effective Membership Inference Attack Exploiting \"All for One\" Principle in Federated Learning","created":"2024-02-09","cited_by_count":12,"area":"safety"},{"id":"2403.17775","title":"Secure Aggregation is Not Private Against Membership Inference Attacks","created":"2024-03-26","cited_by_count":6,"area":"safety"},{"id":"2410.07632","title":"Provable Privacy Attacks on Trained Shallow Neural Networks","created":"2024-10-10","cited_by_count":4,"area":"safety"},{"id":"2203.16463","title":"Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning","created":"2022-03-30","cited_by_count":3,"area":"safety"},{"id":"2410.21453","title":"Inverting Gradient Attacks Makes Powerful Data Poisoning","created":"2024-10-28","cited_by_count":2,"area":"safety"},{"id":"2502.14558","title":"Model Inversion Attack against Federated Unlearning","created":"2025-02-20","cited_by_count":2,"area":"safety"},{"id":"2604.10636","title":"Mitigating Privacy Risk via Forget Set-Free Unlearning","created":"2026-04-12","cited_by_count":2,"area":"safety"},{"id":"2409.13004","title":"Data Poisoning and Leakage Analysis in Federated Learning","created":"2024-09-19","cited_by_count":1,"area":"safety"},{"id":"2507.04771","title":"Efficient Unlearning with Privacy Guarantees","created":"2025-07-07","cited_by_count":1,"area":"safety"},{"id":"2507.23257","title":"Efficient Machine Unlearning via Influence Approximation","created":"2025-07-31","cited_by_count":1,"area":"safety"},{"id":"2507.23291","title":"Evaluating the Dynamics of Membership Privacy in Deep Learning","created":"2025-07-31","cited_by_count":1,"area":"safety"},{"id":"2508.01620","title":"IMU: Influence-guided Machine Unlearning","created":"2025-08-03","cited_by_count":1,"area":"safety"},{"id":"2508.13853","title":"FedUP: Efficient Pruning-based Federated Unlearning for Model Poisoning Attacks","created":"2025-08-19","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":"2512.08443","title":"Fully Decentralized Certified Unlearning","created":"2025-12-09","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":"2606.20553","title":"From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning","created":"2026-06-18","cited_by_count":1,"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":"2507.09602","title":"DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences","created":"2025-07-13","cited_by_count":0,"area":"safety"},{"id":"2511.10936","title":"GraphToxin: Reconstructing Full Unlearned Graphs from Graph Unlearning","created":"2025-11-14","cited_by_count":0,"area":"safety"},{"id":"2601.20325","title":"UnlearnShield: Shielding Forgotten Privacy against Unlearning Inversion","created":"2026-01-28","cited_by_count":0,"area":"safety"},{"id":"2604.16606","title":"SafeLM: Unified Privacy-Aware Optimization for Trustworthy Federated Large Language Models","created":"2026-04-17","cited_by_count":0,"area":"safety"}],"next_cursor":null}