PAEEA: Privacy-Preserving Online Ad Exchange with Efficient Auction for Smart Advertising
Bibliographic record
Abstract
The concern of privacy leakage in online smart advertising is continuously serious for Internet users, and the advertising models are increasingly complex to improve the accuracy of ad impression, which make current privacy-preserving adverting protocols that are based on either profile matching or auction impractical. In this paper, we propose a new privacy-preserving ad exchange protocol (PAEEA) by integrating private set interaction and efficient auction. The distinguished feature of PAEEA is that it enables privacy-preserving matching between the profiles of users and the keywords of ads and private auction based on the bids of advertisers in an efficient way. The bid prices are embedded into ad keywords to produce a private vector, which is matched with the vector of user profiles for ad selection. User profiles, ad keywords, and bid prices are protected to preserve the privacy of both internet users and advertisers. Moreover, the system model of PAEEA follows the model of modern smart advertising, so that PAEEA is applicable to current advertising systems, such as Google Ads. Finally, the performance of PAEEA is demonstrated through extensive experiments that it is computationally efficient and practical to be implemented on smart advertising systems.11A part of the study has been published in Brennan Mosher's Master thesis. The authors own the copyright to the thesis as a whole and it is allowed to republish according to Intellectual Property Guidelines at Queen's University.
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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 | 0.004 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".