FBI v. Apple and beyond: Encryption in the Canadian Law of Digital Search and Seizure
Bibliographic record
Abstract
The stakes have never been higher in the arms race among tech companies, hackers and law enforcement. Tech companies are continually developing measures to enhance the digital security of their users. Hackers and law enforcement agencies, while working toward very different objectives, are themselves developing new techniques to circumvent this encryption in order to access the treasure trove of information contained in digital devices and communications services. In the USA, the fight between the FBI and Apple over the encryption of iPhones has become a flashpoint for this controversy. Tim Cook, the CEO of Apple Inc., has attracted headlines with his highly publicised challenge to court orders obtained by the FBI compelling Apple to assist in unlocking iPhones. This paper examines the implications of the FBI v. Apple dispute in the Canadian context. The authors set out the legal and policy context of the FBI v. Apple debate before exploring the legal dimensions of encryption in Canada. The authors show that the state of Canadian law is unsatisfactory. Clearer safeguards are needed to protect third parties from unduly burdensome law enforcement requests and to protect the privacy of the end users of digital devices and services.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".