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

arXiv cs.CRby Redwanul Karim ()1 min read
AI summary · glm-5.3-flashSummaries by Vani AI

ZK-Trace combines Tardos fingerprints and zero-knowledge credentials to trace leaked classifier copies in federated GNSS monitoring without leaker cooperation.

ZK-Trace addresses leakage of a proprietary classifier distributed to partly trusted stations in federated GNSS interference monitoring, combining public identity marks, recipient-specific Tardos fingerprints, and zero-knowledge credential verification to support offline tracing. The paper provides false-accusation and tracing-score bounds with an interval-arithmetic checker allocating a common budget across accusation and tamper decisions. In a simulated GNSS federation it isolates all 160 single-owner copies and traces 712 of 720 two-owner mixtures with a 0.001 false-naming budget, while feature marks survive feature matching in 20/20 runs at 4.8 percentage-point accuracy cost but are erased by function-only distillation.

Key facts
  • Offline tracing without leaker cooperation via zero-knowledge credentials
  • Isolates 160/160 single-owner copies; traces 712/720 two-owner mixtures
  • Feature mark preserved in 20/20 feature-matching and 19/20 cross-architecture runs
  • Function-only distillation erases feature and weight-space marks
  • Formal false-accusation bounds with executable interval-arithmetic checker
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From the original article

1 min read at arxiv.org

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.

arXiv cs.CR

By Redwanul Karim · arxiv.org

Read the full article at arXiv cs.CR

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