An information theoretic privacy and utility measure for data sanitization mechanisms
Bibliographic record
Abstract
Data collection agencies publish sensitive data for legitimate purposes, such as research, marketing and etc. Data publishing has attracted much interest in research community due to the important concerns over the protection of individuals privacy. As a result several sanitization mechanisms with different notions of privacy have been proposed. To be able to measure, set and compare the level of privacy protection, there is a need to translate these different mechanisms to a unified system. In this paper, we propose a novel information theoretic framework for representing a formal model of a mechanism as a noisy channel and evaluating its privacy and utility. We show that deterministic publishing property that is used in most of these mechanisms reduces the privacy guarantees and causes information to leak. The great effect of adversary's background knowledge on this metric is concluded. We also show that using this framework we can compute the sanitization mechanism's preserved utility from the point of view of a data user. By using the specifications of a popular sanitization mechanism, k-anonymity, we analytically provide a representation of this mechanism to be used for its evaluation.
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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 | 0.001 | 0.011 |
| 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 | 0.010 |
| Open science | 0.015 | 0.032 |
| 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".