Privacy-accuracy trade-offs in noisy digital exposure notifications
Bibliographic record
Abstract
Since the global spread of Covid-19 began to overwhelm the attempts of governments to conduct manual contact-tracing, there has been much interest in using the power of mobile phones to automate the contact-tracing process through the development of exposure notification applications. The rough idea is simple: use Bluetooth or other data-exchange technologies to record contacts between users, enable users to report positive diagnoses, and alert users who have been exposed to sick users. Of course, there are many privacy concerns associated with this idea. Much of the work in this area has been concerned with designing mechanisms for tracing contacts and alerting users that do not leak additional information about users beyond the existence of exposure events. However, although designing practical protocols is of crucial importance, it is essential to realize that notifying users about exposure events may itself leak confidential information (e.g. that a particular contact has been diagnosed). Luckily, while digital contact tracing is a relatively new task, the generic problem of privacy and data disclosure has been studied for decades. Indeed, the framework of differential privacy further permits provable query privacy by adding random noise. In this article, we translate two results from statistical privacy and social recommendation algorithms to exposure notification. We thus prove some naive bounds on the degree to which accuracy must be sacrificed if exposure notification frameworks are to be made more private through the injection of noise.
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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 | 0.047 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".