An anonymization framework for IEC 61850 substation communications: Field-level and topology-aware privacy
Bibliographic record
Abstract
Substation datasets, like those using the IEC61850 standard, hold sensitive information about power flows, equipment statuses, and network configurations. This data could expose vulnerabilities to knowledge-based cyberattacks, making utility providers hesitant to share it publicly for research. While encryption enhances security, it often diminishes the dataset’s utility for research purposes. To address the trade-off between security and utility, we introduce an anonymization technique specifically for the IEC61850 standard, demonstrated on the GOOSE protocol. Our method involves two main approaches: anonymizing sensitive and quasi-identifying fields within packets to preserve data utility, and injecting dummy packets using one of our proposed algorithms to effectively obscure network topology. Using the first method, we publish an anonymized dataset derived from substation communications captured in our testbed to support ongoing research. We evaluated the framework’s effectiveness through a comprehensive communication pattern analysis, including time, flow, statistical, and entropy analyses, and field anonymization testing. Our study highlights the critical importance of maintaining privacy in substation data sharing while ensuring data remains useful for research, setting the foundation for extending this framework across multiple substation protocols in future studies.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".