Blockchain Access Privacy: Challenges and Directions
Bibliographic record
Abstract
Privacy, facilitated by a confluence of cryptography and decentralization, is one of the primary motivations for the adoption of cryptocurrencies like Bitcoin. Alas, Bitcoins privacy promise has proven illusory, and despite growing interest in privacy-centric blockchains, most blockchain users remain susceptible to privacy attacks that exploit network-layer information and access patterns that leak as users interact with blockchains. Understanding if and how blockchain-based applications can provide strong privacy guarantees is a matter of increasing urgency. Many researchers advocate using anonymous communications networks, such as Tor, to ensure access privacy. We challenge this approach, showing the need for mechanisms through which non-anonymous users can (i) publish transactions that cannot be linked to their network addresses or to their other transactions, and (ii) fetch details of specific transactions without revealing which transactions they seek. We hope this article inspires blockchain researchers to think beyond Tor and tackle these important access privacy problems head-on.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.033 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".