Research map / Federated learning and privacy / Differential privacy
Differentially Private Clustering in High-Dimensional Euclidean Spaces
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Private k-means via JL projection, private candidate set, local swap, and noisy-mean recovery | k-means on real and synthetic datasets · MNIST, CIFAR-10 (in3c), Synthetic (d=100) | k-means objective vs k | Helped |
| Increasing data dimension with the proposed method versus SuLQ baseline | private k-means · varying dimension datasets | gap to SuLQ objective | Helped |
| Using epsilon-privacy budget sweep on MNIST (k=10) with proposed method | private k-means · MNIST, k=10 | k-means objective vs epsilon | Helped |
Closest papers by meaning
- Online Differentially Private Consistent Clustering (NeurIPS 2026 · similarity 0.90)
- Making Old Things New: A Unified Algorithm for Differentially Private Clustering (ICML 2024 · similarity 0.90)
- On the Price of Differential Privacy for Hierarchical Clustering (ICLR 2025 · similarity 0.89)
- A Differentially Private Clustering Algorithm for Well-Clustered Graphs (ICLR 2024 · similarity 0.89)
- Differentially Private Federated $k$-Means Clustering with Server-Side Data (ICML 2025 · similarity 0.88)
- Private Geometric Median (NeurIPS 2024 · similarity 0.86)
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