{"total":42,"rows":[{"id":"2107.06641","title":"Trustworthy AI: A Computational Perspective","created":"2021-07-12","cited_by_count":93,"area":null},{"id":"2110.10926","title":"PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion","created":"2021-10-21","cited_by_count":37,"area":null},{"id":"2404.15381","title":"Advances and Open Challenges in Federated Foundation Models","created":"2024-04-23","cited_by_count":36,"area":null},{"id":"2209.10117","title":"A Comprehensive Survey on Trustworthy Recommender Systems","created":"2022-09-21","cited_by_count":32,"area":null},{"id":"2212.06325","title":"AFLGuard: Byzantine-robust Asynchronous Federated Learning","created":"2022-12-13","cited_by_count":29,"area":null},{"id":"2207.10307","title":"Knowledge-enhanced Black-box Attacks for Recommendations","created":"2022-07-21","cited_by_count":26,"area":null},{"id":"2301.00767","title":"A Survey on Federated Recommendation Systems","created":"2022-12-27","cited_by_count":25,"area":null},{"id":"2302.10637","title":"A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy","created":"2023-02-21","cited_by_count":24,"area":null},{"id":"2201.12686","title":"Rank List Sensitivity of Recommender Systems to Interaction Perturbations","created":"2022-01-29","cited_by_count":20,"area":null},{"id":"2204.01499","title":"FedRecAttack: Model Poisoning Attack to Federated Recommendation","created":"2022-04-01","cited_by_count":20,"area":null},{"id":"2402.14836","title":"Stealthy Attack on Large Language Model based Recommendation","created":"2024-02-18","cited_by_count":19,"area":null},{"id":"2206.11433","title":"Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles","created":"2022-06-23","cited_by_count":15,"area":null},{"id":"2402.11637","title":"Poisoning Federated Recommender Systems with Fake Users","created":"2024-02-18","cited_by_count":15,"area":null},{"id":"2202.12993","title":"Projective Ranking-based GNN Evasion Attacks","created":"2022-02-25","cited_by_count":14,"area":null},{"id":"2404.14942","title":"Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures","created":"2024-04-23","cited_by_count":14,"area":null},{"id":"2204.13594","title":"Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios","created":"2022-04-26","cited_by_count":13,"area":null},{"id":"2111.11509","title":"Blockchain-based Recommender Systems: Applications, Challenges and Future Opportunities","created":"2021-11-22","cited_by_count":11,"area":null},{"id":"2311.18244","title":"Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks","created":"2023-11-30","cited_by_count":11,"area":null},{"id":"2107.10457","title":"Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack","created":"2021-07-22","cited_by_count":10,"area":null},{"id":"2101.03042","title":"Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective","created":"2021-01-08","cited_by_count":9,"area":null},{"id":"2311.07127","title":"Multi-agent Attacks for Black-box Social Recommendations","created":"2023-11-13","cited_by_count":9,"area":null},{"id":"2306.16526","title":"Shilling Black-box Review-based Recommender Systems through Fake Review Generation","created":"2023-06-27","cited_by_count":8,"area":null},{"id":"2104.10561","title":"Turning Federated Learning Systems Into Covert Channels","created":"2021-04-21","cited_by_count":6,"area":null},{"id":"2110.15444","title":"10 Security and Privacy Problems in Large Foundation Models","created":"2021-10-28","cited_by_count":6,"area":null},{"id":"2104.09369","title":"Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models","created":"2021-04-19","cited_by_count":5,"area":null},{"id":"2212.06750","title":"FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data","created":"2022-12-13","cited_by_count":5,"area":null},{"id":"2405.15328","title":"Multi-Modal Recommendation Unlearning for Legal, Licensing, and Modality Constraints","created":"2024-05-24","cited_by_count":5,"area":null},{"id":"2205.09673","title":"Detect Professional Malicious User with Metric Learning in Recommender Systems","created":"2022-05-19","cited_by_count":3,"area":null},{"id":"2402.10082","title":"FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning","created":"2024-02-15","cited_by_count":3,"area":null},{"id":"2502.12958","title":"Preventing the Popular Item Embedding Based Attack in Federated Recommendations","created":"2025-02-18","cited_by_count":3,"area":null},{"id":"2210.13762","title":"Towards Robust Recommender Systems via Triple Cooperative Defense","created":"2022-10-25","cited_by_count":1,"area":null},{"id":"2306.07992","title":"Securing Visually-Aware Recommender Systems: An Adversarial Image Reconstruction and Detection Framework","created":"2023-06-11","cited_by_count":1,"area":null},{"id":"2310.16335","title":"Defense Against Model Extraction Attacks on Recommender Systems","created":"2023-10-25","cited_by_count":1,"area":null},{"id":"2403.04954","title":"Fooling Neural Networks for Motion Forecasting via Adversarial Attacks","created":"2024-03-07","cited_by_count":1,"area":null},{"id":"2505.12019","title":"FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients","created":"2025-05-17","cited_by_count":1,"area":null},{"id":"2202.06250","title":"Privacy protection based on mask template","created":"2022-02-13","cited_by_count":0,"area":null},{"id":"2309.17278","title":"Toward Robust Recommendation via Real-time Vicinal Defense","created":"2023-09-29","cited_by_count":0,"area":null},{"id":"2401.11723","title":"Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them","created":"2024-01-22","cited_by_count":0,"area":null},{"id":"2401.12700","title":"Securing Recommender System via Cooperative Training","created":"2024-01-23","cited_by_count":0,"area":null},{"id":"2507.06258","title":"Spattack: Subgroup Poisoning Attacks on Federated Recommender Systems","created":"2025-07-07","cited_by_count":0,"area":null},{"id":"2606.29064","title":"Fairness Attacks on Recommender Systems","created":"2026-06-27","cited_by_count":0,"area":null},{"id":"2610.14487","title":"AlignPoison: Conditional Targeted Poisoning of Generative Recommenders via Decoding-Aligned Prefixes","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}