Bibliographic record
Abstract
Non-independent reasoning (NIR) refers to learning the information of one record from other records, under the assumption that these records share the same underlying distribution. Accurate NIR could disclose private information of an individual. An important assumption made by differential privacy is that NIR is considered to be non-violation of privacy. In this work, we investigate the extent to which private information of an individual may be disclosed through NIR by query answers that satisfy differential privacy. We first define what a disclosure means under NIR by randomized query answers. We then present a formal analysis on such disclosures by differentially private query answers. Our analysis on real life datasets demonstrates that while disclosures of NIR can be eliminated by adopting a more restricted setting of differential privacy, such settings adversely affects the utility of query answers for data analysis, and this conflict can not be easily solved because both disclosures and utility depend on the accuracy of noisy query answers. This study suggests that under the assumption that the disclosure through NIR is a privacy concern, differential privacy is not suitable because it does not provide both privacy and utility.
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 | 0.000 | 0.006 |
| 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 | 0.001 |
| Open science | 0.019 | 0.087 |
| 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".