Blockchain-Based Smart Advertising Network With Privacy-Preserving Accountability
Bibliographic record
Abstract
In a smart advertising network (SAN), a broker builds user profiles from its wealth of user data, manages advertisements for retailers, and disseminates the advertisements through multiple channels. However, the broker sometimes provides insufficient transparency explanations of advertising activities, which may result in the increasing popularity of ad-blocking software and lower advertising investments from retailers. In this paper, we propose a blockchain-based Smart Advertising Network with Privacy-preserving Accountability (SANPA). Specifically, we design a composite Succinct Non-interactive Argument (SNARG) system, that commits advertising policies as cryptographic authenticators in a smart contract. By doing so,SANPAis compatible with the existingSANwithout posing prohibitive implementation cost over the blockchain architecture. Users or retailers can require explanations of an advertising activity by sending a challenge to the smart contract. With the succinctness and privacy preservation of theSNARGsystem, the smart contract can efficiently verify whether the challenged advertising activity follows committed advertising policies without exposing user profile privacy. If any misconduct is identified, the contract enforces public accountability on the misbehaving party by confiscating its cryptocurrency deposits. We conduct extensive experiments to provide both on-chain and off-chain benchmarks, which demonstrates the application feasibility ofSANPA.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".