Research map / Federated learning and privacy / Differential privacy
Practical Differentially Private Top-k Selection with Pay-what-you-get Composition
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
- A Joint Exponential Mechanism For Differentially Private Top-$k$ (ICML 2022 · similarity 0.91)
- Faster Differentially Private Top-$k$ Selection: A Joint Exponential Mechanism with Pruning (NeurIPS 2024 · similarity 0.90)
- Scalable Private Partition Selection via Adaptive Weighting (ICML 2025 · similarity 0.87)
- Privacy Profiles for Private Selection (ICML 2024 · similarity 0.87)
- Instance-Specific Asymmetric Sensitivity in Differential Privacy (NeurIPS 2024 · similarity 0.86)
- Purifying Approximate Differential Privacy with Randomized Post-processing (NeurIPS 2025 · similarity 0.86)
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.
