The Online Advertising industry is seeing a major shift today in its operational constraints with a global movement towards more privacy. Popular techniques for privacy-compliant advertising such as aggregation and differential privacy mechanisms were shown to match high privacy standards but also raise concerns about the possibility to learn relevant machine learning models for ad placement.
We propose in this challenge to explore the trade-off between privacy level and prediction performance, on data donated by Criteo – an industry leader that already released several open datasets for research purposes.
More to read on the challenge on the dedicated AdKDD21 page.