Ushering in a New Era: Assessing the Reasonable Expectation of Privacy vis-à- vis Cryptocurrency and Blockchain Data
Bibliographic record
Abstract
In recent years, the technology of cryptocurrency has become increasingly mainstream and has been documented as playing a role in the commission of contemporary criminal activity. The law must be responsive to these new techniques for committing crimes and adapt accordingly. Currently, there is a dearth of both jurisprudence and literature as it relates to section 8 of the Canadian Charter of Rights and Freedoms and the search and seizure of cryptocurrency by law enforcement. For the protections of section 8 to apply, there must be a reasonable expectation of privacy in the matter searched or seized by authorities. This paper analyzes the reasonable expectation of privacy as it relates to cryptocurrency in three different ways: first, in cryptocurrency transaction data on the blockchain, which is a public ledger that records cryptocurrency transactions; second, in various types of cryptocurrency storage mediums; and third, in user information on cryptocurrency exchanges. Previous section 8 Charter jurisprudence, U.S. case law, secondary sources, and blockchain data were all utilized to guide these analyses. Applying the reasonable expectation of privacy test to these inquiries yielded three distinct findings. It was determined that there is no reasonable expectation of privacy in cryptocurrency transaction data on the blockchain, that there is a reasonable expectation of privacy in various types of cryptocurrency storage mediums, and that there is a reasonable but diminished expectation of privacy in user information on cryptocurrency exchanges.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".