A Comparative Study on Information Privacy Protection Acts in Iran and Selected Countries
Bibliographic record
Abstract
Despite its advantages, expansion of information and communication technologies (ICTs), have increasingly put citizens at the risk of violating their information privacy by governmental or non-governmental organizations. In this article, the legislations advocating information privacy in Iran and selected countries have been comparatively studied and some solutions have been proposed for decreasing the existing gap between Iran and international standards. This article draws on qualitative method, including documentary study, content analysis (with open and axial coding), and comparative study. Statistical population of this study includes 58 countries with information privacy act which 6 countries were selected (Republic of Korea, England, France, Canada, Italy and Ireland) as pioneer countries.The framework for comparative study has 7 dimensions: principles of collection, use, retention and disclosure of data, data subject rights, controller responsibilities and principles of data subject’s accessing to data. According to this study, information privacy protection status in Iran is far from selected countries and international standards. Based on this study, there are two main gaps in protecting citizens’ information privacy in Iran: legislative and supervisory gaps. Iran is far from leading countries in terms of existence of information privacy protection legislation. Leading countries legislations support citizens’ general and sensitive personal data. But Iranian legislations only support the latter one (sensitive personal data) on a limited basis. of 124 identified obligations for protecting information privacy, 81 obligations have been repeated in at least four of six selected countries. There are only 13 of these obligations in Iranian legislations. It seems that due to supervisory gap in Iran, execution of these 13 obligations confronts some problems. It is hoped that the proposed principles in this study could pave a way for enacting rules for protecting information privacy in Iran.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".