arXiv Machine Learning June 16, 2026 By Ziniu Liu, Aiping Li

Cross-Silo De-Anonymization Under Local Differential Privacy: Threat Model, Phase Transition, and Coordination Necessity

Read the original on arXiv Machine Learning →

arXiv:2606. 16763v1 Announce Type: cross Abstract: When a person's records appear in k independent data silos, each protected by (epsilon, delta)-differential privacy, standard composition yields a valid (k*epsilon, k*delta)-DP guarantee for the joint output.

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