Aug 2026· Midwest Symposium on Circuits and Systems· pp. 381-385· 0 citations· 28 references
Abstract
Fault injection attacks on the CRYSTALS-Kyber Number Theoretic Transform (NTT) exploit the recursive structure of the butterfly pipeline to corrupt polynomial computations and recover secret keys via Differential Fault Analysis (DFA). Existing countermeasures protect the entire datapath uniformly, imposing area and power overheads that are prohibitive for resource-constrained deployments. This work presents ADAPT, a stage-aware parity-based architecture that characterizes how transient faults propagate through the Kyber NTT pipeline and exploits the characterization to secure against Differential Fault Analysis with minimal hardware overhead. The architecture provides robust fault detection by utilizing 12 LUTs and 13 flip-flops (FFs), while avoiding the heavy resource requirements typical of Dual Modular Redundancy (DMR). The design, implemented on a Xilinx Artix-7 FPGA, serves as an ultra-lightweight solution for CRYSTALS-Kyber NTT pipelines, maintaining zero negative slack and a minimal $\mathbf{0 . 0 2 \%}$ resource footprint.
Results confirm that permutation-based MBA obfuscation offers a practical, composite, and resilient defense against symbolic execution, balancing strong protection with lightweight performance overhead.
Mo-Xuan Wang, Hai-Yan Hu, Hao-Hang Qin et al.· Journal of computing and sec...· 0 citations
Strong physical unclonable functions (PUFs) are widely used for lightweight device authentication, but their challenge-response mappings are vulnerable to machine-learning modeling attacks. Existing PUFs based on double-latch cells can improve modeling resistance, yet their fixed physical paths limit entropy extraction...
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A Statistical Reduction Monitoring (SRM) method is employed to protect the BM M unit against such adversarial conditions, which incurs minimal hardware overhead while providing efficient detection of both random and deliberate faults in PQC and FHE infrastructures.
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Large language models (LLMs) create significant training bottlenecks on resource-constrained edge devices because of their massive size. Existing federated learning architectures typically require clients to train simultaneously; however, this leads to system crashes due to the high amount of VRAM required by LLMs. Whi...
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FPGAs are increasingly used in cloud computing infrastructures and reconfigurable system-on-chip, particularly for AI acceleration. The availability of FPGAs in cloud data centers has opened up new opportunities for users to improve application performance by implementing customizable hardware accelerators directly on...
Jayeeta Chaudhuri, Hassan Nassar, Dennis R. E. Gnad et al.· ACM Transactions on Design A...· 0 citations
This work identifies homomorphic multiplication as the most error-sensitive operation in practical HE pipelines and characterize how faults propagate and amplify through it, exposing a critical robustness vulnerability and motivating the need for more resilient HE deployments.
Matías Mazzanti, Vattana Chan, Karthik Swaminathan et al.· 0 citations
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