Research map / Federated learning and privacy / Differential privacy
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use Harmonic instead of Quantile estimator on private FM sketch | Private FM sketch · Distinct counting MRE, eps=1.0 | mean relative error | Mixed |
| increase number of sketch copies m | Private FM sketch · Distinct counting MRE | mean relative error | Helped |
| apply differential privacy (eps=1.0) to FM sketch | FM sketch · Distinct counting MRE | mean relative error: at most 2% | No clear effect |
Closest papers by meaning
- Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model (ICML 2025 · similarity 0.92)
- Private Continual Counting of Unbounded Streams (NeurIPS 2025 · similarity 0.90)
- Privately Counting Partially Ordered Data (ICLR 2025 · similarity 0.89)
- Continual Counting with Gradual Privacy Expiration (NeurIPS 2024 · similarity 0.89)
- Differentially Private Set Representations (NeurIPS 2024 · similarity 0.88)
- Perturb-and-Project: Differentially Private Similarities and Marginals (ICML 2024 · similarity 0.88)
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.