arXiv subjects

Tue Nguyen

Publications and source records attributed to Tue Nguyen.

3 recordsLinked to original sources

arXiv2026

EnclaveScale: Hardware-Assisted Edge-DP for Secure Data Centre Power Telemetry

EnclaveScale is a distributed, hardware-assisted telemetry architecture providing post-extraction attestation, enabling operators to collaboratively model high-resolution generative AI power transients. Existing cryptographic techniques scale poorly for 10-Hz streaming or fail to authenticate origins, permitting malicious hosts to spoof sensor inputs. We implement and evaluate a post-extraction pipeline utilizing DCAP attestation, differential privacy noise injection, and Byzantine rejection across 32 GCP Confidential VMs, achieving 0\% post-extraction attack success rate. This edge-DP approach distils continuous GPU transients into discrete Markov-chain transition matrices, guaranteeing event-level differential privacy. To mitigate pre-ingestion vulnerabilities, we propose an SPDM-authenticated first-mile layer. While current platforms lack attested I/O, emerging hardware architectures integrate PCIe IDE and TDISP to natively prevent host-level synthesis, securing the end-to-end provenance boundary. A Global Aggregation Enclave verifies these cryptographic proofs prior to capacity-weighted aggregation. Evaluation demonstrates a steady-state throughput of $131{,}406$ samples/s per enclave, amortising attestation overhead to $0.23\,\mu$s/sample. On empirical NVML-sampled H100, A100, and L4 traces, EnclaveScale achieves a dynamic orchestration margin error of $1.3$\,MW compared to $0.1$\,MW for an honest-aggregator central-DP baseline. EnclaveScale establishes a secure foundation for dynamic multi-tenant power orchestration, obfuscating sub-second anomalies locally and protecting macro-workload confidentiality via spatial dilution during global aggregation.

cs.CR↗

arXiv2026

Architectural Limits of Cloud TPUs in Finite-Field Cryptography

We empirically characterise the cost-efficiency deficit between cloud Tensor Processing Units and GPUs for finite-field cryptography. Against A100 GPU baselines (cuZK), we measure a $[5{,}558\times, 6{,}908\times]$ deficit across v5p and v4 architectures under an FP32-mantissa staging discipline, and a $\sim$$4{,}693\times$ deficit using v5p's native \texttt{int32} accumulator. We analytically project this deficit into a fundamental arithmetic penalty (lacking wide-integer ALUs) and a spatial penalty. We demonstrate that evaluating concurrent multi-tenant deployments, where strict separation forces eager Montgomery reduction, yields a projected $5.19\times$ spatial collapse; relaxing this constraint theoretically recovers these spatial cycles, yet the underlying arithmetic penalty remains. To facilitate this characterisation, we deploy \codename as a measurement vehicle. By mapping low-degree polynomials onto matrix-form Number Theoretic Transforms, the scheduler stacks heterogeneous polynomials into dense 2D matrices, achieving $\sim$$100\%$ K-dimension column occupancy on uniform workloads ($>$$92\%$ on mixed-degree traces). However, despite optimal K-dimension packing, severe M-dimension under-utilisation (e.g., $6.25\%$ on v4) combined with overwhelming VPU-bound Montgomery reduction stalls mathematically starve the systolic arrays. A post-hoc HLO validator ensures these measurements remain structurally isolated against the XLA fusion engine. Our findings empirically demonstrate the structural inadequacy of AI-optimised systolic arrays for exact, high-throughput field arithmetic.

cs.AR↗

arXiv2026

Enforcing Attestable Workflows across Untrusted Networks

Confidential high-performance computing orchestrates workloads across federated domains, yet existing frameworks rely on high-overhead user-space library operating systems or assume single-host execution. We propose \codename, an architecture federating Trusted Execution Environments via a split Trusted Computing Base (TCB) design. It couples a hardware-isolated Control Plane executing Mutually Attested Key Exchange (\make) with a measured guest-resident extended Berkeley Packet Filter (eBPF) Data Plane. By anchoring cryptographic key release to hardware measurements and executing enforcement in the kernel, \codename\ achieves native-speed encrypted routing. Empirical evaluation demonstrates a steady-state enforcement cost of $6\,\mu$s per packet, imposing a $13$--$15\,\mu$s absolute latency overhead. On distributed pipelines, \codename\ incurs just a $6.1\%$ execution penalty over plaintext baselines, bypassing the $62\%$ penalty of user-space counterparts. The system initializes a 100-node cluster in under 1.5 seconds, providing an efficient confidential interconnect for long-running workflows.

cs.CR↗