Research map / Federated learning and privacy / Differential privacy
DP-GenG: Differentially Private Dataset Distillation Guided by DP-Generated Data
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Optimal Differentially Private Model Training with Public Data (ICML 2024 · similarity 0.88)
- SynBench: A Benchmark for Differentially Private Text Generation (NeurIPS 2026 · similarity 0.88)
- Efficiently Computing Similarities to Private Datasets (ICLR 2024 · similarity 0.88)
- Analysis of Differentially Private Synthetic Data: A Measurement Error Approach (AAAI 2024 · similarity 0.87)
- Does Training with Synthetic Data Truly Protect Privacy? (ICLR 2025 · similarity 0.87)
- Differentially Private Relational Learning with Entity-level Privacy Guarantees (NeurIPS 2025 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.