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Due to the ongoing deprecation of third-party cookies on mainstream browsers, the digital advertising industry is facing novel challenges regarding how to operate artificial intelligence (AI) systems. One of these bottlenecks lies in the tentative use of local differential privacy (LDP) to obfuscate granular user data, preventing from using standard machine learning pipelines to tackle the privacy/utility trade-off. This position paper reviews the main research directions that have been explored to cope with this issue and states the main positioning and research guidelines regarding how to operate an AI system under LDP, notably by pointing out the main limitations of existing work. More specifically, we highlight the importance of conducting research works focusing on multi-task learning under LDP schemes and of seeking prior information to help design privacy-preserving mechanisms.

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Open Research Challenges for Private Advertising Systems Under Local Differential Privacy

  • 会议论文

  • 发表日期：

    2025年

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  • Matilde Tullii,
  • Solenne Gaucher,
  • Hugo Richard,
  • Eustache Diemert,
  • Vianney Perchet,
  • Alain Rakotomamonjy,
  • Clément Calauzènes,
  • Maxime Vono

摘要

Due to the ongoing deprecation of third-party cookies on mainstream browsers, the digital advertising industry is facing novel challenges regarding how to operate artificial intelligence (AI) systems. One of these bottlenecks lies in the tentative use of local differential privacy (LDP) to obfuscate granular user data, preventing from using standard machine learning pipelines to tackle the privacy/utility trade-off. This position paper reviews the main research directions that have been explored to cope with this issue and states the main positioning and research guidelines regarding how to operate an AI system under LDP, notably by pointing out the main limitations of existing work. More specifically, we highlight the importance of conducting research works focusing on multi-task learning under LDP schemes and of seeking prior information to help design privacy-preserving mechanisms.