Research map / Federated learning and privacy / Differential privacy
On Differentially Private Graph Sparsification and Applications
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| private sparse graph output instead of a noisy symmetric matrix or cut-only graph, for cut queries | private graph sparsification meta-algorithm · cut queries (theory) | additive error | Helped |
| answer cut queries on the privatized sparse graph rather than the privatized graph of Gupta et al. | cut-query answering · graphs with sublinear error (theory) | additive error | Helped |
| apply post-processing algorithms to the private sparsifier for MAX-CUT and SPARSEST-CUT | private sparsifier · MAX-CUT, SPARSEST-CUT (theory) | additive error | Helped |
Closest papers by meaning
- Breaking the $n^{1.5}$ Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition (ICML 2025 · similarity 0.92)
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- Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust (NeurIPS 2024 · similarity 0.86)
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