Anonymizing web cookies with differential privacy
Bibliographic record
Abstract
Cookie synchronization enables multiple advertising networks to share a user's cookie data, thereby refining ad targeting without explicit user consent.While existing mitigation strategies such as cookie blockers reduce privacy risks, they may also diminish website revenue or restrict certain online services.To address this limitation, we introduce a cookie anonymizing man-in-the-middle (MITM) proxy, which we call CSyncProxy, that leverages local differential privacy, specifically the exponential mechanism, to grant users granular control over the amount of data shared during cookie synchronization.By obfuscating user identities across websites, the proxy maintains sufficient personalization for advertisements while safeguarding user privacy.Experimental webcrawls of the top 100 websites indicate that our approach reduces the occurrences of successful cookie synchronization by up to 40% without disrupting normal site functionality.i a privilege to work under his supervision and to be a part of the Data Mining and Security (DMaS) lab.I am deeply grateful to the supporters of this work, Daniel Migault, Stere Preda, and Amine Boukhtouta, from Ericsson Canada, in partnership with MITACS.Their support and collaboration were instrumental in making this research possible.I also thank Ericsson
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".