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arXiv Machine Learning By Longzhu He, Li Sun, Hao Peng, Ruijie Wang, Raymond Chi-Wing Wong, Sen Su

Trust-But-Verify: Poisoning-Resilient Locally Private Graph Learning Protocols

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The paper introduces VERITAS, a poisoning‑resilient protocol for locally private graph learning that combines local differential privacy with a trust‑but‑verify approach. VERITAS employs a verification list to encode peer trust, performs local data perturbation, server‑side malicious node pruning, dual denoising, and robust private graph learning. Experiments on four real‑world datasets show that VERITAS defends against data poisoning attacks while improving downstream graph learning utility under strict privacy guarantees.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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