A TEE-Guarded Data Management System for Time-Scale Data in Industrial Internet of Things
Bibliographic record
Abstract
With the prosperity of the Industrial Internet of Things (IIoT), concerns have arisen about its energy efficiency and data security. A manufacturer, especially a medium or small one, usually depends partially or fully on third-party providers for IIoT infrastructures (e.g., cloud services, edge devices, IIoT applications), leading to concerns about trusted and confidential data processing. Moreover, the processed IIoT data may include personal information (e.g., employee status), introducing privacy compliance concern as well. Data protection depends on trust, which can be achieved through distributed trust (e.g., blockchains) or centralized trust (e.g., Trusted Third Parties (TTPs)). However, the energy cost for trust is high, as the former requires extra redundancy and the latter introduces workload transfer to the TTP. Fortunately, trusted execution environment (TEE) technologies provide a more efficient solution for trust. A TEE enables efficient, confidential, and protected execution while establishing centralized trust via remote attestation of executables. This paper proposes a TEE-based data management architecture for IIoT, inspired by an extensive and secure personal data management system, but with a reduced trusted computing base (TCB). The proposed architecture is feasible for time-scale data in IIoT, which are only appended over time and never updated, such as machine status monitoring data. A single-threaded SGX-based prototype of the data access component in the architecture is implemented for the time-scale data scenario. Benchmarks and evaluations are provided to demonstrate the prototype performance for time-scale data and the potential TCB reduction of the proposed design. The proposal reveals a more verifiable and feasible integration of TEE-based trusted data processing in an IIoT data management system, with reduced TCB, high efficiency, and security, under a strong threat model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".