Ransomware, Privacy, and Data Protection: A Multi-Jurisdictional Analysis
Bibliographic record
Abstract
In recent years thousands of organisations have fallen victim to ransomware attacks. This malicious software disables access to users’ data and demands payment of a ransom for its restoration. Cyberattacks like these are usually thought of in the context of cybercrime, but because the data affected by ransomware is often personal data, such attacks also raise pertinent questions that need to be examined under the light of data privacy laws. Considering that security has always been central to the protection of personal data, this chapter proposes an analysis of ransomware attacks through the lens of the well-established information security model, i.e. the CIA (confidentiality, integrity, and availability) triad. Using these three basic security principles, we examine whether ransomware will be considered a data breach under data privacy laws and what the legal implications of such breaches are. In order to illustrate these points, we will focus on ransomware attacks that target organisations that process personal data and highlight three examples of jurisdictions, namely the European Union (EU), Canada and Israel.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".