Beyond content analysis: Detecting targeted ads via distributed counting

TL;DR

This paper presents iWnder, a privacy-preserving crowdsourcing system that detects targeted online ads in real time, including indirect targeting, by sharing global ad statistics without compromising individual user privacy.

Contribution

The paper introduces a novel privacy-preserving protocol for detecting targeted ads through crowdsourcing, capable of identifying indirect targeting campaigns.

Findings

iWnder accurately detects targeted ads in real time

The system preserves user privacy while sharing global ad data

It successfully identifies indirect ad targeting campaigns

Abstract

Being able to check whether an online advertisement has been targeted is essential for resolving privacy controversies and implementing in practice data protection regulations like GDPR, CCPA, and COPPA. In this paper we describe the design, implementation, and deployment of an advertisement auditing system called iWnder that uses crowdsourcing to reveal in real time whether a display advertisement has been targeted or not. Crowdsourcing simplifies the detection of targeted advertising, but requires reporting to a central repository the impressions seen by different users, thereby jeopardising their privacy. We break this deadlock with a privacy preserving data sharing protocol that allows iWnder to compute global statistics required to detect targeting, while keeping the advertisements seen by individual users and their browsing history private. We conduct a simulation study to explore the effect of different parameters and a live validation to demonstrate the accuracy of our approach. Unlike previous solutions, iWnder can even detect indirect targeting, i.e., marketing campaigns that promote a product or service whose description bears no semantic overlap with its targeted audience.