Common intrusion factors and improvement measures based on case study of privacy impact assessment
Bibliographic record
Abstract
As the Internet becomes increasingly widespread, various cybercrimes involving privacy, such as misuse, abuse, and leakage of privacy in integrated information systems that contain privacy data, are increasing worldwide. To address such intrusions, privacy impact assessments (PIAs) of information systems have been performed. Various studies, including PIAs, have been conducted to establish PIA frameworks (PIAF), surveys, and analyses to investigate intrusion cases. Impact assessments based on assessment items and low-level PIAs that analyze intrusion factors differ among countries. The PIAF in the Netherlands comprises three stages, that in Canada comprises four stages, and that in Korea comprises three stages, each of which defines subprocesses. In this study, the PIAFs of these countries were investigated and compared. We also compared the assessment items of ISO/IEC 27701 and Korea's PIAF. We analyzed PIAs conducted on information systems operated by public institutions in Korea. Through the analysis of these PIAs, we derived the factors causing intrusions in Korea and proposed improvement points for each intrusion factor. We expect that these analysis results can be effectively applied to other countries.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".