ZK-Trace: Certified Collusion Tracing with Zero-Knowledge Credentials for Federated GNSS Interference Monitoring

arXiv 2609.08763 ↗First posted 8 Sep 2026cs.CR cs.LGcited by 0

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Abstract (arXiv)

Federated global navigation satellite system (GNSS) monitoring distributes a proprietary classifier to partly trusted stations, any of which may leak its copy. ZK-Trace combines public identity marks, recipient-specific Tardos fingerprints, and zero-knowledge credential verification. The registry supports offline tracing without the leaker's cooperation. We establish conditional false-accusation bounds for arbitrary recovered bit patterns, a finite completeness bound under a hidden-bias residual channel, and a deterministic tracing-score bound for correlated feature-distillation errors. An interval-arithmetic checker makes the conditional bound executable and allocates a common budget across accusation and tamper decisions. Under innocent-row independence, the certificate-based evaluation uses a false-naming budget of 0.001 per investigation. It isolates all 160 single-owner copies and traces 712 of 720 two-owner mixtures without naming an innocent. Experiments use a simulated GNSS federation and CIFAR-10. Feature matching preserves the feature mark in 20/20 runs and cross-architecture transfer in 19/20, at copy-accuracy costs of 4.8 and 6.1 percentage points on GNSS and CIFAR-10. Function-only distillation erases the feature mark, and distillation also removes weight-space marks. These results support verifiable tracing under explicit statistical and cryptographic assumptions. Credential knowledge and recipient evidence serve distinct roles.

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References · 18

2015Deep Residual Learning for Image Recognitioncited by 52,096
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2014FitNets: Hints for Thin Deep Netscited by 1,653
2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
2020Entangled Watermarks as a Defense against Model Extractioncited by 149
2019DAWN: Dynamic Adversarial Watermarking of Neural Networkscited by 100
2021SoK: How Robust is Image Classification Deep Neural Network Watermarking? (Extended Version)cited by 69
2020WAFFLE: Watermarking in Federated Learningcited by 37
2021FedIPR: Ownership Verification for Federated Deep Neural Network Modelscited by 36
2022FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Modelcited by 30

6 more

2024Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localizationcited by 11
2023Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learningcited by 10
2023FedZKP: Federated Model Ownership Verification with Zero-knowledge Proofcited by 9
2024Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classificationcited by 7
2025FedPoP: Federated Learning Meets Proof of Participationcited by 1
2026BlackCATT: Black-box Collusion Aware Traitor Tracing in Federated Learningcited by 1

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