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FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof
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Federated learning (FL) allows multiple parties to cooperatively learn a federated model without sharing private data with each other. The need of protecting such federated models from being plagiarized or misused, therefore, motivates us to propose a provable secure model ownership verification scheme using zero-knowledge proof, named FedZKP. It is shown that the FedZKP scheme without disclosing credentials is guaranteed to defeat a variety of existing and potential attacks. Both theoretical analysis and empirical studies demonstrate the security of FedZKP in the sense that the probability for attackers to breach the proposed FedZKP is negligible. Moreover, extensive experimental results confirm the fidelity and robustness of our scheme.
Forward citations
Cited by 2 Pith papers
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Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates
Succinct zero-knowledge proofs can certify that a fine-tuned model differs from a base model only by norm-bounded, low-rank, or sparse parameter drift, with cost set by that structure rather than model size.
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zkSTAR: A zero knowledge system for time series attack detection enforcing regulatory compliance in critical infrastructure networks
zkSTAR proves with zero-knowledge proofs that a utility's Kalman-filter-based attack alarms were computed correctly, keeping sensor data private.
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