Privacy Law Issues in Blockchains: An Analysis of PIPEDA, the GDPR, and Proposals for Compliance
Bibliographic record
Abstract
The purpose of this research is to identify how blockchain technologies may clash with, and reconcile, privacy legislation. The research identifies legislative issues related to the data subjects’ privacy rights, and proposes technical and policy oriented solutions for privacy law compliance. Part one defines blockchain technology and distinguishes between blockchain forms to identify the nuances of public, private, and hybrid blockchains. Part two outlines the primary privacy law challenges to blockchain as a database and medium of exchange, using Canada’s PIPEDA and the European Union’s GDPR as a benchmark for legal analysis. Part three offers a non-technical primer of privacy-centric technologies designed to facilitate the compliant processing of data on public blockchains; this section also discusses analytical techniques used to identify users on public blockchains. Part three also explores the implications of privacy-centric technologies as a solution to legislative compliance. Part four offers policy recommendations designed to complement proposed technical safeguards and generate a system of accountability in a network characterized by anonymity.
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 | 0.030 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| 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".