Local Node Differential Privacy

Name: Local Node Differential Privacy
Uploaded: 2026-10-05T14:58:36.849073+00:00
Duration: 654 s
Description: This work develops a framework for answering graph queries privately using Local Node Differential Privacy (LNDP*). It introduces a 'blurry degree distribution' ($ ext{ddf}_s G$) to approximate true graph statistics while ensuring privacy. The method balances inherent approximation error from blurring with noise error, proving the framework is asymptotically tight for specific applications.

This work develops a framework for answering graph queries privately using Local Node Differential Privacy (LNDP*). It introduces a 'blurry degree distribution' ($ ext{ddf}_s G$) to approximate true graph statistics while ensuring privacy. The method balances inherent approximation error from blurring with noise error, proving the framework is asymptotically tight for specific applications.

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