Robust Privacy-Preserving Cloud-Based Control Using Reed-Solomon Codes
Bibliographic record
Abstract
Cloud-based networked control systems are emerging as a promising approach for managing complex processes by offloading control tasks to remote cloud platforms. While this architecture offers flexibility and scalability, it also raises significant privacy concerns, particularly regarding the sensitive measurements transmitted from the plant to third-party cloud providers, where the control algorithms are executed. In multi-cloud setups, an effective privacy-preserving solution is provided by the Shamir secret sharing scheme. When applied to networked control systems, this approach allows the measurement vector (the secret) to be split among multiple clouds without compromising the overall control law, which can be successfully and efficiently reconstructed from computations performed on the clouds. However, existing Shamir-based schemes have limited robustness, as they only ensure the reconstruction of the correct control input in the presence of missing shares but cannot handle cases where some shares are corrupted. This work presents a robust privacy-preserving computational scheme for multi-cloud control systems using a robust Reed-Solomon secret sharing scheme. The proposed solution enables the reconstruction of the control input despite missing or corrupted shares while preserving the secrecy of the measurements sent to the clouds. The effectiveness and potential of this approach are demonstrated through simulations involving a remotely controlled differential-drive robot.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".