Building an End-to-End De-Identification Pipeline for Advertising Activity Data at LinkedIn

Advertising platforms rely heavily on activity data to measure and optimize ads performance. With current privacy regulations and platform requirements, LinkedIn is held to increasingly rigorous standards in the handling of our members' personal data. This is acute for our ads business, as we adhere to strict regulations that necessitate stringent measures when handling user data, including data minimization, which is almost expanding into a global requirement. These regulations continue to evolve, requiring constant adaptation to new standards, while our data pipelines were originally established in a time when the use of personal data was less regulated.

Motivated by the principle of building privacy by design, we undertook a comprehensive project involving numerous stakeholders to address these challenges, and built an end-to-end robust pipeline that de-identifies advertising activity data. The goal of this project was to ensure that user information is protected while still enabling processing on this de-identified data to generate valuable analytics and enable advertisers to learn the effectiveness of their ad spend. We have onboarded products such as performance reporting and billing as the hero use-cases onto this pipeline. This talk will cover the design, implementation and innovative aspects of this pipeline. We will discuss the various privacy enhancing technologies we applied, our system architecture, challenges faced such as scalability (to process billions of events a day) and balancing privacy with the needs of the business. Finally, we will also highlight the outcomes and practical insights gained from this project.