safety security
Private Network Counts Reach the Noninteractive Limit
A local node-differential-privacy algorithm counts any constant-size template subgraph with near-optimal additive error.
Summary
A local node-differential-privacy algorithm counts any constant-size template subgraph with near-optimal additive error.
The matching lower bound establishes the first polynomial-in-n separation between central and local node privacy for accuracy on all input graphs. The result is theoretical and expands beyond prior degree-distribution statistics; it is not a deployment benchmark.
Why it matters
A local node-differential-privacy algorithm counts any constant-size template subgraph with near-optimal additive error.
Limits and context
- The result is theoretical and expands beyond prior degree-distribution statistics; it is not a deployment benchmark.
Key claims
A local node-differential-privacy algorithm counts any constant-size template subgraph with near-optimal additive error.
Qualification: The result is theoretical and expands beyond prior degree-distribution statistics; it is not a deployment benchmark.
Evidence: source-2026-10-17-019
Sources
- arXiv preprint 2610.22278, original v1arXiv · primary research
Corrections
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