Protection Profile of Personal Information Security System: Designing a Secure Personal Information Security System
Bibliographic record
Abstract
As cyber-crimes using personal information such as ID theft are increasing, there is a need for appropriate technology or law to protect privacy. To this end, the Korean Government established the Privacy Act on March 29th 2011. The Privacy Act prescribes a specification for dealing with privacy with the intention to protect personal information from being collected, leaked, misused, or abused so that it can improve rights and interests of the nation and eventually realize the dignity and value of man. The United States, Japan, Canada, and several countries of the EU have their own privacy law being established or revised. Although there must be differences depending on the circumstances of each country, the ultimate goal of the privacy law should be the same. Consequently, there might be the same or similar technical protection required by all these countries. Between the increasing interest in protecting personal information and the establishment of the Privacy Act, many industries are having relevant products released one after another. Customers without knowledge of the law and the product types cannot decide what they need. This paper intends to derive necessary security functions of a personal information security system based on the Common Criteria and analyze the limit of the products in order to make guidelines for privacy and information protection system.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".