A data-free membership inference attack reconstructs images from federated learning updates using standard cell library layouts as priors, allowing inference of hardware characteristics such as circuit layers and technology nodes from reconstruction fidelity.
Enhance membership inference attacks in federated learning,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CR 2years
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DECIFR shows that public standard cell library layouts enable a no-auxiliary-data membership inference attack on federated gradient updates by correlating reconstruction quality with training membership in integrated circuit datasets.
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A Data-Free Membership Inference Attack on Federated Learning in Hardware Assurance
A data-free membership inference attack reconstructs images from federated learning updates using standard cell library layouts as priors, allowing inference of hardware characteristics such as circuit layers and technology nodes from reconstruction fidelity.
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DECIFR: Domain-Aware Exfiltration of Circuit Information from Federated Gradient Reconstruction
DECIFR shows that public standard cell library layouts enable a no-auxiliary-data membership inference attack on federated gradient updates by correlating reconstruction quality with training membership in integrated circuit datasets.