On the Optimal Number of Grids for Differentially Private Non-Interactive $K$-Means Clustering
Differentially private $K$-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because the released data synopsis can be reused for other downstream tasks without additional privacy loss. The choice of the number of grids for discretizing the data points is crucial, as it directly controls the quantization bias and the amount of noise injected to preserve privacy. The widely adopted strategy selects a grid size that is independent of the number of clusters and also relies on empirical tuning. In this work, we revisit this choice and pro...
| Authors | Gokularam Muthukrishnan, Anshoo Tandon |
|---|---|
| Primary category | cs.CR |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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