Research map / Federated learning and privacy / Differential privacy
Differentially Private Clustering via Maximum Coverage
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use maximum coverage across distance thresholds, followed by a non-private clustering algorithm on privatized counts | Private k-medians · k-medians | additive error: O(k\log n) vs O(k \log n)^{1.5} | Helped |
| Use maximum coverage-based selection on potential centers before the final private clustering step | Private Euclidean k-means · Euclidean k-means | additive error: O\big(k\log n + d^{0.51} (\log\log n)^{2.53} (k\log k )^{1.01} \big) vs O\big((k \log \left(n\log k\right))^{1.5} + d^{0 | Helped |
| Use the proposed approach with a constant-approximation non-private k-medians algorithm as the black box | Private k-medians · k-medians | multiplicative approximation factor: 6.35+\epsilon vs 6 | Hurt |
Closest papers by meaning
- Making Old Things New: A Unified Algorithm for Differentially Private Clustering (ICML 2024 · similarity 0.92)
- Online Differentially Private Consistent Clustering (NeurIPS 2026 · similarity 0.91)
- Differentially Private Federated $k$-Means Clustering with Server-Side Data (ICML 2025 · similarity 0.89)
- On the Price of Differential Privacy for Hierarchical Clustering (ICLR 2025 · similarity 0.88)
- A Differentially Private Clustering Algorithm for Well-Clustered Graphs (ICLR 2024 · similarity 0.87)
- Private Geometric Median (NeurIPS 2024 · similarity 0.86)
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