Ontario's New Invasion of Privacy Torts: Do They Offer Monetary Redress for Violations Suffered via the Internet of Things?
Bibliographic record
Abstract
In the age of the Internet of Things, we are all susceptible to countless privacy violations. Society’s prevalent use of interconnected devices enables companies to collect and manipulate users’ personal data for their own monetary benefits. While the law grapples with how best to protect users from such privacy risks, another significant danger has emerged: by besting the often-weak security measures employed by companies that create interconnected devices, hackers can compromise the integrity of these electronics by remotely accessing them. This allows hackers to gain unauthorized access to the personal data of users and potentially hold their devices for ransom.\nSmart devices are constantly evolving, and the risk that their users’ personal information will be misused is increasing. However, Ontario law has been slow to acknowledge the possibility for individuals to obtain damages for breaches of their privacy. To date, Ontario courts have only recognized two invasion of privacy torts: the Intrusion Upon Seclusion and the Public Disclosure of Private Facts, both of which are very limited in scope. \nThe aim of this paper is to examine whether these torts are sufficiently broad to address the privacy breaches that have become commonplace in our digital world. After outlining the privacy violations to which users of the Internet of Things are exposed, this paper will examine the potential of applying the Intrusion and Public Disclosure torts to such practices in an attempt to determine if users might be successful in obtaining monetary redress for such violations in a manner that the privacy legislation has been unable to achieve. This paper will then analyze whether or not these same torts might be used to impose liability on the companies responsible for personal information or devices that were breached by hackers. Finally, suggestions for how Ontario privacy law should evolve to better address our technologically enhanced reality will be explored.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| 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".