The Impact of Encryption Technologies on Criminal Investigations in Canada: A Balanced Approach to the 'Going Dark' Problem in Light of Self-Incrimination and Privacy Considerations
Bibliographic record
Abstract
Encryption, a method of concealing information from unwanted eyes, has recently become more prevalent in society, following revelations of massive surveillance conducted by governments and the increasing number of attacks on companies holding their customers’ digitized information. Encryption mechanisms have become more sophisticated and widely used by citizens who wish to keep their personal information private and secure. Conversely, criminals have also been using strong encryption mechanisms to hide their wrongdoing, which has made it harder for law enforcement officials to access evidence. This “going dark” phenomenon has impacted both the seizure of “data at rest” (i.e., data that is saved on a device) and the access to “data in transit” (i.e., communication data that is still being transmitted over a network). By examining the technological underpinning of encryption technology and its beneficial impacts on society, this thesis proposes an analytical framework that would allow law enforcement to compel suspects to decrypt their data or devices in specific situations and under strict conditions. This framework is crafted to reflect the unique Canadian experience with the self-incrimination and to harmonize this principle with the protection against unreasonable search and seizure, both found within the Canadian Charter of Rights and Freedoms. Inspiration is drawn from comparable legal systems found within Australia, the United States, and the United Kingdom, while transnational and international considerations are also examined due to the inherent borderless nature of the internet. Essentially, this thesis submits that alternatives to compelled decryption by suspects should be favoured to address the “going dark” problem and that strong encryption should remain available to the public. It is submitted that Parliament should create a strict framework applicable to compelled decryption which would allow law enforcement access to “data at rest” in its decrypted form, when no other alternative exists. It is also submitted that resorting to “lawful hacking” as a method of circumventing encryption applied to “data in transit” should be examined and regulated by Parliament.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".