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arXiv2026

Side-Channel Attack Prevention for Post Quantum Cryptography Algorithms: A 3D-Shield Approach

Despite their theoretical quantum attack resistance, practical implementations of post-quantum cryptography (PQC) algorithms remain susceptible to side-channel attacks. This includes all standard algorithms selected the National Institute of Standards and Technology (NIST) such as Kyber/ML-KEM, Dilithium/ML-DSA, Falcon/FN-DSA and SPHINCS+/SLH-DSA. This paper proposes an alternative defense for side-channel resilience based on 3D integration that uses 3D hardware to shield the underlying cryptographic activity by introducing decoy keys and physical shielding through stacking. The proposed approach can complement other security measures (such as masking and shuffling) on protecting Number Theoretic Transform (NTT) that move computation to dedicated NTT chips. We outline the construction of our approach and validate the effectiveness on a generic attack targeting intermediate computation inside an NTT butterfly.

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arXiv2026

Enhancing Edge AI System Security with a Stacked Hardware Security Controller for Robust and Energy-Efficient Anomaly Detectio

In recent years, the growing adoption of Edge AI systems has been accompanied by a surge in security threats. Edge AI deployments face an rapidly evolving spectrum of security threats, from side-channel information leakage to ad- versarial model manipulation and physical device tampering. Conventional security mechanisms such as malware scanners and resource-intensive firewall solutions are unsuitable due to edge AIs typically limited computational resources, strict energy budgets and thermal constraints especially in battery-powered and field-deployed environments. This paper proposes a stacked, hardware-based security mechanism that enables low-overhead, continuous detection of anomalous execution patterns. Anomaly detection utilizing hard- ware performance counters and physical sensors outperforms software-based alternatives by providing key capabilities such as tamper-resistant root-of-trust isolation, negligible runtime overhead, and resilience against compromised operating systems. Experimental analysis shows that adversarial or malicious ap- plications produce observable deviations in sensor and thermal readings on Edge AI systems and hardware-based anomaly detection provides effective and low-overhead anomaly detection.

cs.CR↗