An evaluation framework for privacy‐preserving solutions applicable for blockchain‐based internet‐of‐things platforms
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
Abstract Blockchain‐based applications provide many promising opportunities to overcome the challenges associated with the Internet of Things (IoT) ecosystems (eg, centralized architecture, data integrity, and reliability). In particular, blockchain technology offers many desirable features for IoT infrastructures, such as decentralization, trustworthiness, trackability, and immutability. However, while logging all transactions in a distributed blockchain ledger provides transparency, it also makes it possible to compromise user's privacy, thus posing a grand challenge to IoT architects and implementers. Over the past years, a set of solutions have been proposed for various scenarios, to address these privacy issues. In this paper, we survey these solutions, classify, and analyze their advantages and disadvantages. We also introduce an evaluation framework to evaluate the quality of the privacy‐preserving based on an adjustable weighting scheme. Finally, we rate the analyzed solutions based on their privacy ranks, and hope our evaluation can shed light on the future design of privacy‐preserving solutions applicable for blockchain‐based IoT platforms.
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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 | 0.001 | 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 it