Research map / Federated learning and privacy / Differential privacy
Differentially Private Linear Sketches: Efficient Implementations and Applications
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add zero-mean Gaussian noise at initialization | CountSketch · Zipf and CAIDA frequency estimation | average relative error | No clear effect |
| Add zero-mean Gaussian noise at initialization | CountSketch · Zipf and CAIDA top 10 identification | F1 score | No clear effect |
| Add positively offset Gaussian noise at initialization to preserve no-underestimation | Count-Min · Zipf top 10 identification | F1 score: 1.0 | Helped |
| Increase space budget | Private Count-Min · Frequency estimation | average relative error | Hurt |
| Decrease privacy budget | Private Count-Min · Frequency estimation | average relative error | Hurt |
| Decrease privacy budget | Private DCS · Zipf and CAIDA quantile approximation | average rank error | Hurt |
| Increase the number of evenly spaced quantiles | Private DCS · Zipf and CAIDA quantile approximation | average rank error | No clear effect |
| Use a larger data universe | Private DCS · CAIDA versus Zipf quantile approximation | average rank error | Hurt |
Closest papers by meaning
- Distributed Differentially Private Data Analytics via Secure Sketching (ICML 2025 · similarity 0.88)
- The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches (NeurIPS 2025 · similarity 0.88)
- Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model (ICML 2025 · similarity 0.87)
- Lightweight Protocols for Distributed Private Quantile Estimation (ICML 2025 · similarity 0.86)
- On Differential Privacy for Adaptively Solving Search Problems via Sketching (ICML 2025 · similarity 0.86)
- Privacy Guarantees in Posterior Sampling under Contamination (NeurIPS 2026 · similarity 0.85)
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