International Association
for Cryptologic Research

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 candidates, 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 compute over sensitive web browsing data that users would otherwise be more hesitant to provide. We collect data us- ing a custom-built Chrome browser extension and perform 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 pro- portions (LLP) problem in machine learning, which allows us to train on unlabeled web browsing data using publicly available polling and elec- tion results as the ground truth. The client code is open source, and the remaining code will be open source in the future.