@inproceedings{BuxbaumEtAl:CSCML25,
  author	= {Buxbaum, Samuel and Tassis, Lucas and Boschelli, Lucas and Christenson, Dino and Comarela, Giovanni and Crovella, Mark and Varia, Mayank},
  title		= {Privacy-Preserving Machine Learning on Web Browsing for Public Opinion},
  booktitle	= {Proceedings of The International Symposium on Cyber Security, Cryptology and Machine Learning (CSCML)},
  year		= {2025},
  abstract	= {We present a real-world deployment of secure multiparty computation to predict political preference from private web browsing data. To estimate aggregate preferences for the 2024 U.S. presidential election, we collect and analyze secret-shared data from nearly 8000 users from August 2024 through February 2025, with over 2000 daily active users sustained throughout the bulk of the survey. The use of MPC allows us to access sensitive web browsing data that users would otherwise be more hesitant to provide. We collected data using a custom-built Chrome browser plugin and performed our analysis using the CrypTen MPC library. To our knowledge, we provide the first implementation under MPC of a model for the learning from label proportions (LLP) problem in machine learning, which allows us to train on unlabeled web browsing data using publicly available polling and election results as the ground truth.},
  doi		= {TBD},
  URL		= {TBD}
}