Continuous Location Statistics Sharing Algorithm with Local Differential Privacy
Bibliographic record
Abstract
Continuous sharing of location statistics produces valuable knowledge to understand important phenomena, such as popular places or pattern behaviors. Most importantly, data should be shared without jeopardizing users' privacy. Differential privacy becomes de-facto technique for private statistical data release. Much work focuses on the centralized setting where users send their original data to a trusted server. Then the server adds controlled noises to generate differentially private statistics. This centralized approach is vulnerable to attacks where an adversary may access the true data by attacking the trusted server. Local differential privacy neutralizes this type of attacks by allowing each user to obfuscate their data before it reaches the server for statistical analysis. In this paper, we propose an algorithm to share location statistics that leverages local differential privacy combined with w-event privacy. Our solution guarantees the user's privacy when continuously releasing statistics over infinite streams. Experimental evaluation on real-life data shows our solution with strong privacy guarantee.
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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 | 0.000 | 0.001 |
| 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 | 0.017 | 0.044 |
| 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".