Author

Pritish Kamath

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Preprint Sep 2026

Overcoming the Randomness-Utility Trade-off in Answering Differentially Private Linear Queries

We study the question of answering linear queries with differential privacy using few (expected) random bits. We provide a randomness-efficient analog of the $\| \cdot \|_K$-norm mechanism of Hardt and Talwar [HT10]. For the $\ell_\infty$-error, our algorithm can answer $d$ linear queries with $O(d / \varepsilon)$ erro...

Surendra Ghentiyala, Pritish Kamath, Ravi Kumar et al. · 0 citations

Preprint Sep 2026

Differential Privacy Meets Fixed Parameter Tractability: Algorithms and Lower Bounds

We study combinatorial optimization problems under the constraint of $\epsilon$-differential privacy ($\epsilon$-DP). Given the strong lower bounds for explicitly outputting solutions, we work within the implicit representation framework of Gupta et al. (SODA 2010), where a private polynomial-time randomized"encoder"ge...

Pritish Kamath, Ravi Kumar, Pasin Manurangsi · 1 citation

Preprint Aug 2026

Online Differentially Private Consistent Clustering

A generic reduction is given that transforms the (sensitive) input stream into a private stream, which is a semi-coreset of the input stream, which implies that any (non-private) online clustering algorithm, run as a post-processing step, can achieve good utility for the original clustering objective.

Edith Cohen, Vadym Doroshenko, Badih Ghazi et al. · 0 citations