Personal Data Ecosystem (PDE) – A Privacy by Design Approach to an Individual's Pursuit of Radical Control
Bibliographic record
Abstract
Personal data in the networked world is considered “the new oil” – its collection is said to enhance user experience but is in the control and for the profit of others, leading to a lack of transparency and erosion of privacy. Expectations surrounding what constitute a healthy privacy-protective relationship between individuals and organizations are being reset under the umbrella of the emerging Personal Data Ecosystem (PDE). The PDE is supported by new technologies and services, such as Personal Data Vaults (PDV) and data sharing platforms. These technologies and services allow individuals to control and manage their own information. While PDE developments are positive from a privacy perspective given the control they provide to the individual, in the wrong hands, one's PDV and activities within the PDE could be exploited as a major surveillance tool. The paper introduces Privacy by Design (PbD) which the author sees as essential to the success of the PDE. For several years, the Information and Privacy Commissioner of Ontario, Canada, has examined emerging technologies and best practices that are relevant to the PDE, which can assist in developing the PDE in a manner consistent with PbD. By following PbD, privacy in the PDE can indeed be assured.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".