Attribute Inference from Interactive Targeted Ads
Targeted advertising systems can pair audiences selected by advertisers with ad units that expose visible user actions. When an interaction remains linked to the campaign that elicited it, the advertiser may receive an observation tied to a user rather than only an aggregate report. We model that channel as a noisy oracle for attribute inference. The model separates targeting predicates, exposure, interaction, and disclosure. These boundaries capture the gap between eligibility and delivery, and the gap between interaction and advertiser visibility. We build a reproducible benchmark using synthetic populations calibrated with public data, each with known sensitive labels. A generated camp...
| Authors | Peihao Li |
|---|---|
| Primary category | cs.AI |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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