iCAT+: An Interactive Customizable Anonymization Tool Using Automated Translation Through Deep Learning
Bibliographic record
Abstract
Data anonymization is a viable solution for data owners to mitigate their privacy concerns. However, existing data anonymization tools are inflexible to support various privacy and utility requirements of both data owners and data users. In most cases, this limitation is due to a lack of understanding of those requirements as well as the non-customizability of the existing tools. To address this limitation, we proposeiCAT+, which is an interactive and customizable anonymization approach. More specifically, we first automate the interpretation of data owners’ and data users’ textual requirements by deploying a Convolutional Neural Network (CNN) model for Natural Language Processing (NLP). Second, we introduce the concept of theanonymization spaceto model possible combinations of per-attribute anonymization primitives based on the level of privacy and utility that each primitive provides. Third, we design an ontology model that maps the translated requirements into their appropriate anonymization primitives in the defined anonymization space corresponding to the plain data. Fourth, we evaluate the efficiency and effectiveness ofiCAT+based on both real and synthetic network data. Finally, we assess its usability through a real user study involving participants from industry and research laboratories. Our experiments show the effectiveness and efficiency of our solution (e.g., requirement translation accuracy of 99% at the data owner side and 98% at the data user side, with a computational time of around one minute for the Google cluster dataset).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".