The Risk-Utility Tradeoff for Data Privacy Models
Bibliographic record
Abstract
Nowadays with growth of information technologies, organizations are constantly collecting information about individuals. Public availability of these datasets can considerably benefit the society. To ensure data privacy of a released dataset, various privacy models have been introduced. While many privacy models and techniques have been proposed for data sanitization, the area of sanitized data evaluation has received less attention. This paper investigates the four most well-known data privacy models: k-anonymity, l-diversity, t-closeness, and , ϵ-differential privacy. We evaluate the data utility (usefulness of sanitized data) and the disclosure risk (re-identification risk of an individual) of the sanitized data for each model. We use a combination of several data utility and risk metrics to measure the impact of a privacy parameter (e.g., k, ϵ) on a particular privacy model. This enables us to compare the risk-utility tradeoff of semantic privacy models such as , ϵ-differential privacy to the early syntactic models such as k-anonymity on the same scale. We used the Adult dataset from the UCI machine learning repository to conduct our experiments. Experimental results show that , ϵ-differential privacy outperforms other privacy models in terms of both data utility and disclosure risk.
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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 | 0.002 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.112 | 0.178 |
| 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".