Compliance evaluation of information privacy protection in e-government systems in Anglophone West Africa using ISO/IEC 29100:2011
Bibliographic record
Abstract
Due to various reasons, only few researchers focused their investigations on the current status of information security and privacy protection of e-Government services in Africa. This paper attempts to partially fill the gap by reporting on the compliance evaluation of privacy protection in e-Government systems in the countries of Anglophone West Africa, namely in Ghana, Nigeria, Liberia, Sierra Leone and Gambia. In the countries, e-Government services have become one of the most important and efficient means by which government interacts with citizens. The ways to facilitate information privacy protection in e-Government systems of a given country include enactment of a comprehensive information privacy regulation. The regulation serves as a legal framework that considers internationally accepted privacy protection principles, such as those of the ISO/IEC 29100:2011, and applicable guidelines of the U.S. NIST SP 800-53 Rev.4. In this paper, the privacy principles of the ISO/IEC 29100:2011 serve as a baseline for evaluation of the content of privacy protection regulations of the Anglophone West African countries. The paper also reports results of a passive security reconnaissance performed on selected e-Government websites. While the paper acknowledges recent progresses made in the area of privacy protection in the countries of Anglophone West Africa, recommendations are provided to mitigate the identified gaps.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".