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New research reveals de-anonymization phase transition in multi-silo DP

By PulseAugur Editorial · [2 sources] ·

A new research paper introduces a framework called cross-silo person-level DP (XSP-DP) to analyze de-anonymization risks when data is split across multiple silos, each protected by differential privacy. The study identifies a critical threshold, approximately Theta(log n / epsilon^2), where de-anonymization becomes feasible as the number of data silos increases. The findings suggest that even if individual data silos offer privacy, combining information from multiple silos can lead to inevitable de-anonymization without explicit coordination. AI

IMPACT Establishes a theoretical threshold for de-anonymization risks in distributed private datasets, impacting future privacy-preserving system designs.

RANK_REASON Research paper published on arXiv detailing a new theoretical framework and findings on data privacy.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ziniu Liu, Aiping Li ·

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

    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. This worst-case bound…

  2. arXiv cs.LG TIER_1 English(EN) · Aiping Li ·

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

    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. This worst-case bound, however, does not answer the concrete inference …

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