Privacy impact assessment (PIA) guideline for securing personal data
Bibliographic record
Abstract
Privacy Impact Assessment (PIA) is a tool to assess the potential effects or impacts onto a privacy of a project, initiative, system, or even scheme which involve on the handling of indivisuals’ personal data. This tool is used to mitigate or avoid the identified risks through a series of activities. However, PIA is not being widelyimplemented and in fact, PIA can be considered new in United Kingdom (UK) after Australia, Canada, Hong Kong, and Ireland started. Besides UK, the other countries have started to reach their PIA maturity and because of that, their established PIA guidelines being studied by the experts and researchers to understand on the PIA processes being implemented by those countries. This project is meant to propose a PIA guideline to be implemented in any small-scale electronic systems that involved in handling personal information which in return will be very helpful in assessing the potential risks that might compromised the privacy of those personal data. In order to design the proposed PIA guideline, it is crucial to conduct a thorough study on this field by analysing the existing PIA guidelines, researches of this area, and also other relevant resources. The biggest challenge in this project lies in selecting the best activities and number of PIA steps to be included in the proposed guideline due to the absence of an international PIA standard and also the difference needs and requirements of organisations. In conjunction to that, a comparison and mapping activities will be conducted which in the end will result to the selection of the appropriate activities and number of steps for the proposed guideline. The proposed guideline will then need to be validated by the experts of this field to obtain a feedback which will help to further enhance the proposed guideline. Finally, the final draft of the guideline will be designed by analysing the given feedbacks by the experts of this area
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".