Search papers, labs, and topics across Lattice.
This paper introduces DECIFR, a novel two-stage Membership Inference Attack (MIA) that leverages domain-specific knowledge of standard cell library layouts (SCLL) to compromise the privacy of integrated circuit (IC) training data in Federated Learning (FL). DECIFR uses a guided Gradient Inversion Attack (GIA) to reconstruct training images from model updates, demonstrating that reconstruction fidelity correlates with membership status. The attack requires no auxiliary dataset and reveals a critical vulnerability in standard FL protocols when applied to hardware assurance.
Domain-specific knowledge, like standard cell library layouts, turns federated learning into a privacy nightmare for integrated circuit training data, enabling high-fidelity reconstruction attacks without auxiliary datasets.
Federated Learning (FL) is a promising approach for multiparty collaboration as a privacy-preserving technique in hardware assurance, but its security against adversaries with domain-specific knowledge is underexplored. This paper demonstrates a critical vulnerability where available standard cell library layouts (SCLL) can be exploited to compromise the privacy of sensitive integrated circuit (IC) training data. We introduce DECIFR, a novel two-stage Membership Inference Attack (MIA) that requires no auxiliary dataset. The attack employs a guided Gradient Inversion Attack (GIA) to reconstruct a client's training images from intercepted model updates. Our findings reveal that the fidelity of these reconstructions directly correlates with membership status, allowing an adversary to reliably distinguish members from non-members based on image quality. This work exposes a practical threat that overcomes the limitations of conventional attacks and underscores that standard FL protocols are insufficient for securing domains with extensive knowledge. We conclude that robust defenses are essential for the secure application of FL in hardware assurance.
