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

2026-06-08

AI Summary

This work addresses the limitations of existing encrypted telemetry schemes, which struggle to support high-frequency (10 Hz) power data streams and lack robust source authentication, rendering them vulnerable to spoofing by malicious hosts. To overcome these challenges, the authors propose a distributed hardware-assisted telemetry architecture that integrates DCAP remote attestation, event-level differential privacy, and SPDM-based authentication to establish a secure first-mile layer. The design further incorporates Byzantine fault tolerance and GPU enclave-based global verification to enable traceable, extraction-attack-resistant, high-resolution AI modeling of power transients. Experimental results demonstrate that the system achieves 0% success rate against post-extraction attacks across 32 GCP Confidential VMs, with a per-enclave throughput of 131,406 samples/second and an authentication overhead of merely 0.23 microseconds per sample. On H100/A100/L4 platforms, it attains a dynamic scheduling error of 1.3 MW, significantly outperforming centralized differential privacy baselines.

Abstract

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 $0.23\,μ$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.

Problem

• power telemetry
• data authenticity
• scalability
• sensor spoofing
• secure aggregation

Innovation

• hardware-assisted telemetry
• edge differential privacy
• post-extraction attestation
• Markov-chain transition matrices
• confidential computing