Law Enforcement Access to Encrypted Data: Legislative Responses and the Charter
Bibliographic record
Abstract
In our digital age, encryption represents both a tremendous social benefit and a significant threat to public safety. While it provides the confidence and trust essential for digital communications and transactions, wrongdoers can also use it to shield incriminating evidence from law enforcement, potentially in perpetuity. There are two main legal reforms that have been proposed to address this conundrum: requiring encryption providers to give police “exceptional access” to decrypted data, and empowering police to compel individuals decrypt their own data. This article evaluates each of these alternatives in the context of policy and constitutional law. We conclude that exceptional access, though very likely constitutional, creates too great a risk of data insecurity to justify its benefits to law enforcement and public safety. Compelled decryption, in contrast, would provide at least a partial solution without unduly compromising data security. And while it would inevitably attract constitutional scrutiny, it could be readily designed to comply with the Charter. By requiring warrants to compel users to decrypt and giving evidentiary immunity to the act of decryption, our proposal would prevent inquisitorial fishing expeditions yet allow the decrypted information itself to be used for investigative and prosecutorial purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.075 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.027 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 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".