A TEE-Guarded Data Management System for Time-Scale Data in Industrial Internet of Things
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it