Towards Private and Efficient Ad Impression Aggregation in Mobile Advertising
Bibliographic record
Abstract
In the secure mobile advertising, mobile users privately select advertisements of interest for displaying without exposing their preferences to the ad network. However, the strong privacy guarantee has uncovered limitations on gathering aggregated ad impression statistics for the ad network to enforce correct billing on the merchants who run their ad campaigns. Early efforts integrated cryptographic voting mechanism to address this challenge, which introduces additional bandwidth overhead on mobile devices due to the construction of the ballot proof. In this paper, we propose a private and efficient ad impression aggregation scheme in mobile advertising to protect the individual ad impression statistics while preventing the ad-fraud attack. The main idea of the proposed scheme is the design of an efficient cryptographic voting mechanism based on the compact hamming weight proof technique and additive homomorphic encryption. The proposed scheme has better bandwidth efficiency by reducing the ballot proof size from O(logN) to O(1), where N denotes the dimension of the ballot. Security analysis demonstrates the confidentiality of the individual impression statistics and the verifiability of the ballot proof under standard cryptographic assumptions. Experimental results consolidate that the proposed scheme is feasible for real-world implementations on mobile devices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".