{"total":19,"rows":[{"id":"2408.08868","title":"A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs","created":"2024-08-16","cited_by_count":9,"area":null},{"id":"2507.01129","title":"On Design Principles for Private Adaptive Optimizers","created":"2025-07-01","cited_by_count":6,"area":null},{"id":"2509.14275","title":"FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health","created":"2025-09-16","cited_by_count":5,"area":null},{"id":"2512.16059","title":"ContextLeak: Auditing Leakage in Private In-Context Learning Methods","created":"2025-12-18","cited_by_count":4,"area":null},{"id":"2503.12314","title":"Empirical Privacy Variance","created":"2025-03-16","cited_by_count":3,"area":null},{"id":"2510.05288","title":"DP-Adam-AC: Privacy-preserving Fine-Tuning of Localizable Language Models Using Adam Optimization with Adaptive Clipping","created":"2025-10-06","cited_by_count":3,"area":null},{"id":"2602.17861","title":"JAX-Privacy: A library for differentially private machine learning","created":"2026-02-19","cited_by_count":3,"area":null},{"id":"2409.17144","title":"Differential Privacy Regularization: Protecting Training Data Through Loss Function Regularization","created":"2024-09-25","cited_by_count":1,"area":null},{"id":"2506.15307","title":"SecP-Tuning: Efficient Privacy-Preserving Prompt Tuning for Large Language Models via MPC","created":"2025-06-18","cited_by_count":1,"area":null},{"id":"2508.18911","title":"Enhancing Model Privacy in Federated Learning with Random Masking and Quantization","created":"2025-08-26","cited_by_count":1,"area":null},{"id":"2509.12958","title":"Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning","created":"2025-09-16","cited_by_count":1,"area":null},{"id":"2510.07304","title":"Cocoon: A System Architecture for Differentially Private Training with Correlated Noises","created":"2025-10-08","cited_by_count":1,"area":null},{"id":"2601.22124","title":"Toward Federated Large Language Models in Medicine: A Parameter-Efficient Framework for Privacy-Preserving, Multi-Institutional Adaptation","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2603.23472","title":"Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions","created":"2026-03-24","cited_by_count":1,"area":null},{"id":"2503.03622","title":"It's My Data Too: Private ML for Datasets with Multi-User Training Examples","created":"2025-03-05","cited_by_count":0,"area":null},{"id":"2506.16460","title":"Black-Box Privacy Attacks on Shared Representations in Multitask Learning","created":"2025-06-19","cited_by_count":0,"area":null},{"id":"2507.05610","title":"On the Inherent Privacy of Zeroth Order Projected Gradient Descent","created":"2025-07-08","cited_by_count":0,"area":null},{"id":"2510.10990","title":"Secret-Protected Evolution for Differentially Private Synthetic Text Generation","created":"2025-10-13","cited_by_count":0,"area":null},{"id":"2512.01748","title":"SA-ADP: Sensitivity-Aware Adaptive Differential Privacy for Large Language Models","created":"2025-12-01","cited_by_count":0,"area":null}],"next_cursor":null}