Online Advertising with Verifiable Fairness
Bibliographic record
Abstract
Online advertising is a popular business model where advertisers can deliver promotional marketing messages to their potential consumers via Ad brokers. However, as the proxy between advertisers and customers, a malicious Ad broker could arbitrarily fabricate the advertising rates to overcharge advertisers, which causes unnecessary financial loss. To deal with this issue, we propose a publicly verifiable and fair online advertising scheme. Specifically, a proof-of-downloading (PoD) protocol is first designed based on the zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK), to help the customer generate a unique acknowledgment for downloading the Ad; the acknowledgment will then be published to both the advertiser and the Ad broker such that anyone can verify the acknowledgment to guarantee the fairness and transparency of online advertising. Moreover, as long as the customer's private key is not leaked, our scheme can resist the collusion attack, i.e., the Ad broker and the customer collude with each other to deceive the advertiser, which has not been addressed in previous works. Finally, we evaluate the performance of the proposed scheme to demonstrate its computational efficiency.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".