Towards an Understanding of Privacy Management Architecture in Big Data: An Experimental Research
Bibliographic record
Abstract
Abstract Big data analytics provide valuable information allowing organizations to gain insights that grant them a competitive advantage in the market. However, it also provides access to data that compromise people's privacy. The development of sophisticated technologies for data analysis has resulted in a growing concern around privacy management in big data. While many sites (e.g. Facebook) require the user to provide personal information to access their services, others (e.g. Google search) can automatically capture or trace user activities and use that data to acquire personal demographic information. Therefore, Internet users are – willingly or unwillingly – constantly disclosing sensitive personal information. In addition, users do not get a complete picture of how their personal information is disseminated online. In this paper, we investigate information privacy through an experiment using large‐scale disclosure of personal web activity data to track fragments of personal information released over a period of time. This experiment gives a clear picture of the potential privacy losses of individual users based on released personal information and activities at different websites. By devising an enterprise architecture using a privacy‐by‐design framework, this study provides a useful guide to addressing the managerial challenges of privacy management.
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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 | 0.045 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".