Information theoretic-based privacy risk evaluation for data anonymization
Bibliographic record
Abstract
Aim: Data anonymization aims to enable data publishing without compromising the individuals’ privacy. The reidentification and sensitive information inference risks of a dataset are important factors in the decision-making process for the techniques and the parameters of the anonymization process. If correctly assessed, measuring the reidentification and inference risks can help optimize the balance between protection and utility of the dataset, as too aggressive anonymization can render the data useless, while publishing data with a high risk of de-anonymization is troublesome. Methods: In this paper, a new information theoretic-based privacy metric (ITPR) for assessing both the re-identification risk and sensitive information inference risk of datasets is proposed. We compare the proposed metric with existing information theoretic metrics and their ability to assess risk for various cases of dataset characteristics. Results: We show that ITPR is the only metric that can effectively quantify both re-identification and sensitive information inference risks. We provide several experiments to illustrate the effectiveness of ITPR. Conclusion: Unlike existing information theoretic-based privacy metrics, the ITPR metric we propose in this paper is, to the best of our knowledge, the first information theoretic-based privacy metric that allows correctly assessing both re-identification and sensitive information inference risks.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".