arXiv:2606.15209v1 Announce Type: new Abstract: 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 visibili
The increasing sophistication and pervasiveness of targeted advertising systems, coupled with growing concerns over data privacy, makes this research timely.
This research highlights a significant vulnerability in how user data can be inferred from interactive targeted ads, impacting privacy, regulation, and ethical AI development.
Advertisers may gain more granular, user-specific data than previously assumed, shifting power dynamics in the digital advertising ecosystem and requiring new privacy safeguards.
- · Privacy researchers
- · Security auditors
- · Consumer privacy advocates
- · Ad platforms relying on opaque data practices
- · Users with weak privacy protections
- · Advertisers misusing granular data
Increased scrutiny and potential regulation on the data collection and inference capabilities of targeted advertising platforms.
Development of new privacy-preserving advertising technologies and standards to mitigate attribute inference risks.
Shifts in consumer behavior towards platforms that offer stronger privacy guarantees, impacting market share in the digital advertising sector.
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Read at arXiv cs.AI