DIFFERENTIAL PRIVACY IN DIGITAL ADVERTISING: BALANCING AUDIENCE INSIGHTS AND USER PRIVACY

Abstract

This article explores the application of differential privacy in audience analytics for digital advertising, addressing the critical challenge of balancing effective targeting with user privacy protection. We investigate various differential privacy mechanisms, including the Laplace, Exponential, and Gaussian mechanisms, and their applicability to audience insights. Through comprehensive experimentation and analysis, we demonstrate the trade-offs between privacy guarantees and insight granularity, showing how advertisers can maintain valuable audience understanding while adhering to strict privacy standards. Our findings reveal that differentially private approaches, while introducing some noise and reducing data granularity, can preserve overall advertising effectiveness while significantly enhancing user privacy and trust. We discuss implementation strategies for advertisers, including the integration of differential privacy into existing analytics pipelines and techniques for querying and interpreting privacy-preserved datasets. The article also examines the broader implications for the digital advertising ecosystem, considering regulatory compliance, ethical considerations, and the potential for industry-wide adoption. Our research contributes to the growing body of knowledge on privacy-preserving technologies in advertising and provides practical insights for stakeholders navigating the complex landscape of user privacy and data-driven marketing. By demonstrating the viability of differential privacy in audience analytics, this work paves the way for a more privacy-conscious approach to digital advertising that aligns with evolving regulatory requirements and user expectations.