{"total":28,"rows":[{"id":"2103.00710","title":"Towards Personalized Federated Learning","created":"2021-03-01","cited_by_count":464,"area":null},{"id":"2201.08164","title":"From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI","created":"2022-01-20","cited_by_count":302,"area":null},{"id":"2405.16821","title":"Perturbation-Restrained Sequential Model Editing","created":"2024-05-27","cited_by_count":28,"area":null},{"id":"2208.09967","title":"Inferring Sensitive Attributes from Model Explanations","created":"2022-08-21","cited_by_count":15,"area":null},{"id":"2309.16398","title":"Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey","created":"2023-09-28","cited_by_count":12,"area":null},{"id":"2303.12942","title":"A Survey on Explainable Artificial Intelligence for Cybersecurity","created":"2023-03-07","cited_by_count":9,"area":null},{"id":"2105.01959","title":"Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning","created":"2021-05-05","cited_by_count":7,"area":null},{"id":"2210.17376","title":"SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability","created":"2022-10-31","cited_by_count":7,"area":null},{"id":"2302.08044","title":"Balancing Privacy Protection and Interpretability in Federated Learning","created":"2023-02-16","cited_by_count":6,"area":null},{"id":"2312.04542","title":"SoK: Unintended Interactions among Machine Learning Defenses and Risks","created":"2023-12-07","cited_by_count":6,"area":null},{"id":"2404.00673","title":"A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures","created":"2024-03-31","cited_by_count":6,"area":null},{"id":"2311.14137","title":"Privacy-Preserving Algorithmic Recourse","created":"2023-11-23","cited_by_count":5,"area":null},{"id":"2503.01942","title":"Mathematical Foundation of Interpretable Equivariant Surrogate Models","created":"2025-03-03","cited_by_count":5,"area":null},{"id":"2204.01049","title":"A Differentially Private Framework for Deep Learning with Convexified Loss Functions","created":"2022-04-03","cited_by_count":4,"area":null},{"id":"2312.16191","title":"SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning","created":"2023-12-22","cited_by_count":4,"area":null},{"id":"2202.11748","title":"The Need for Interpretable Features: Motivation and Taxonomy","created":"2022-02-23","cited_by_count":3,"area":null},{"id":"2201.12648","title":"Private Boosted Decision Trees via Smooth Re-Weighting","created":"2022-01-29","cited_by_count":2,"area":null},{"id":"2204.12903","title":"Spending Privacy Budget Fairly and Wisely","created":"2022-04-27","cited_by_count":2,"area":null},{"id":"2305.02474","title":"MLHOps: Machine Learning for Healthcare Operations","created":"2023-05-04","cited_by_count":2,"area":null},{"id":"2404.06144","title":"Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability","created":"2024-04-09","cited_by_count":2,"area":null},{"id":"2408.05060","title":"GLEAMS: Bridging the Gap Between Local and Global Explanations","created":"2024-08-09","cited_by_count":2,"area":null},{"id":"2505.02828","title":"Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review","created":"2025-05-05","cited_by_count":2,"area":null},{"id":"2412.20798","title":"Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry","created":"2024-12-30","cited_by_count":1,"area":null},{"id":"2507.04528","title":"Towards integration of Privacy Enhancing Technologies in Explainable Artificial Intelligence","created":"2025-07-06","cited_by_count":1,"area":null},{"id":"2510.20621","title":"Towards the Formalization of a Trustworthy AI for Mining Interpretable Models explOiting Sophisticated Algorithms","created":"2025-10-23","cited_by_count":1,"area":null},{"id":"2511.09775","title":"Privacy-Preserving Explainable AIoT Application via SHAP Entropy Regularization","created":"2025-11-12","cited_by_count":1,"area":null},{"id":"2212.07508","title":"Tensions Between the Proxies of Human Values in AI","created":"2022-12-14","cited_by_count":0,"area":null},{"id":"2609.37667","title":"Where Privacy Belongs: Placement Diagnosis and Certified Selection for Private Counterfactual Explanations on Graphs","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}