REGULATORY BARRIERS TO PERSONAL DATA MANAGEMENT. AN ANALYTICAL REVIEW OF INTERNATIONAL LEGAL REGIMES
Bibliographic record
Abstract
Significance. In the context of global digital transformation, personal data management, especially in such a sensitive area as healthcare, faces significant regulatory and legal barriers. The key challenge is the ambiguity of regulation related to the rights of data subjects, and the legal basis for data processing. Different jurisdictions demonstrate a range of approaches to solving this issue, from strict European standards to models with an emphasis on state control or corporate responsibility, complicating international cooperation and preventing innovation in data management. Purpose. To undertake a comparative analysis of international legal regimes for personal data management, aimed at identifying common regulatory barriers and determining the scope of restrictions on the rights of subjects in socially significant areas. Material and methods. The work is based on a comparative legal analysis of documents regulating personal data protection in key jurisdictions: the European Union (GDPR), Asia (PDPA of Singapore, PDPO of Hong Kong, DPDP Act of India, PIPL of China) and North America (CCPA/HIPAA of the USA, PIPEDA of Canada). The methodology includes a legal analysis of articles of regulations and laws, a synthesis of data processing principles, and identification of general and specific legal barriers. Results. It has been established that there are three main components that shape universal regulatory barriers: 1) the scope of rights of data subjects and their limitations for the public interest; 2) strict requirements for operators to protect, minimize and limit processing purposes; 3) differentiation of regulation for the public and private sectors. It has been revealed that even in strict regimes like GDPR, legislation provides for flexible data processing mechanisms without direct consent for research purposes. Conclusion. The analysis demonstrates that modern regulation of personal data management strikes a balance between protecting individual rights and promoting the public interest. The identified universal contours of regulatory barriers and the exceptions stipulated by legislation for scientific and medical purposes can be taken into account within the Russian jurisdiction to develop a well-balanced legal model that meets the challenges of digital transformation of healthcare. Keywords: personal data; legal regulation; comparative legal analysis; GDPR; digital transformation in healthcare.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".