Private attributes: The meanings and mechanisms of “privacy-preserving” adtech

Abstract

This study analyzes the meanings and technical mechanisms of privacy that leading advertising technology (adtech) companies are deploying under the banner of “privacy-preserving” adtech. We analyze this discourse by examining documents wherein Meta, Google, and Apple each propose to provide advertising attribution services—which aim to measure and optimize advertising effectiveness—while “solving” some of the privacy problems associated with online ad attribution. We find that these solutions define privacy primarily as anonymity, as limiting access to individuals’ information, and as the prevention of third-party tracking. We critique these proposals by drawing on the theory of privacy as contextual integrity. Overall, we argue that these attribution solutions not only fail to achieve meaningful privacy but also leverage privacy rhetoric to advance commercial interests.