Design and Specification of a Privacy-Preserving Registration for Blockchain-Based Energy Markets
Bibliographic record
Abstract
The challenges of climate change and the related demand to integrate non-plannable and weather-dependent renewable energy resources pose enormous challenges for the entire energy domain, e.g. in the context of grid control. These challenges reveal the need for new technical solutions and new business models while they indicate the required and inevitable transition to smart grids. Many blockchain-based solutions are being discussed in this context, ranging from peer-to-peer energy trading to grid-serving applications. However, especially in connection with public blockchains, clear security privacy challenges arise since the security and privacy of private data must be guaranteed while traceability must be avoided. Therefore, in this paper, we will specify privacy-protecting registration processes for blockchain-based flexibility markets that enable pseudonymous access to the latter. Furthermore, in collaboration with a governmental regulating institution named DGA, we will show that using an existing X.509-based PKI and RSA-based cryptographic processes, the integrity of all market participants can be guaranteed. This integrity is essential for the security-critical use of operating reserve. In addition, we will evaluate the specified processes in terms of efficiency, scalability, security, and privacy protection.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".