Privacy-Preserving Ad Targeting in the Age of Data Protection Legislation: Leveraging Federated Learning and Clean Room Solutions

The digital advertising ecosystem faces unprecedented transformation driven by global privacy regulations that restrict traditional data collection and targeting practices. As regulations like GDPR and CCPA redefine permissible data handling, advertisers and publishers must adopt privacy-preserving technologies to maintain targeting effectiveness while ensuring compliance. This article examines two promising solutions: data clean rooms and federated learning. Data clean rooms provide secure environments where multiple parties can collaborate on analytics without exposing raw data, utilizing differential privacy, secure multi-party computation, and homomorphic encryption to enable valuable insights while protecting user information. Federated learning offers a distributed computational framework where models train across decentralized devices without centralizing sensitive data, keeping information at its source while enabling personalization. The comparative assessment reveals distinct advantages for each approach: clean rooms excel in cross-dataset analysis while federated learning provides superior privacy for individual-level data with on-device processing. Future directions indicate potential integration with blockchain technologies, advancement of standardization efforts, and development of formal optimization frameworks that balance privacy protection with targeting effectiveness. As the advertising landscape evolves, these privacy-preserving technologies will reshape industry practices, enabling personalized advertising that respects user privacy and regulatory requirements.