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Taeweon Suh

Publications and source records attributed to Taeweon Suh.

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Decentralized Translator of Trust: Supporting Heterogeneous TEE for Critical Infrastructure Protection

Trusted execution environment (TEE) technology has found many applications in mitigating various security risks in an efficient manner, which is attractive for critical infrastructure protection. First, the natural of critical infrastructure requires it to be well protected from various cyber attacks. Second, performance is usually important for critical infrastructure and it cannot afford an expensive protection mechanism. While a large number of TEE-based critical infrastructure protection systems have been proposed to address various security challenges (e.g., secure sensing and reliable control), most existing works ignore one important feature, i.e., devices comprised the critical infrastructure may be equipped with multiple incompatible TEE technologies and belongs to different owners. This feature makes it hard for these devices to establish mutual trust and form a unified TEE environment. To address these challenges and fully unleash the potential of TEE technology for critical infrastructure protection, we propose DHTee, a decentralized coordination mechanism. DHTee uses blockchain technology to support key TEE functions in a heterogeneous TEE environment, especially the attestation service. A Device equipped with one TEE can interact securely with the blockchain to verify whether another potential collaborating device claiming to have a different TEE meets the security requirements. DHTee is also flexible and can support new TEE schemes without affecting devices using existing TEEs that have been supported by the system.

cs.CR

TPU as Cryptographic Accelerator

Cryptographic schemes like Fully Homomorphic Encryption (FHE) and Zero-Knowledge Proofs (ZKPs), while offering powerful privacy-preserving capabilities, are often hindered by their computational complexity. Polynomial multiplication, a core operation in these schemes, is a major performance bottleneck. While algorithmic advancements and specialized hardware like GPUs and FPGAs have shown promise in accelerating these computations, the recent surge in AI accelerators (TPUs/NPUs) presents a new opportunity. This paper explores the potential of leveraging TPUs/NPUs to accelerate polynomial multiplication, thereby enhancing the performance of FHE and ZKP schemes. We present techniques to adapt polynomial multiplication to these AI-centric architectures and provide a preliminary evaluation of their effectiveness. We also discuss current limitations and outline future directions for further performance improvements, paving the way for wider adoption of advanced cryptographic tools.

cs.CR