Privacy-Preserving Machine Learning on Web Browsing for Public Opinion

Sam Buxbaum; Lucas M. Tassis; Lucas Boschelli; Giovanni Comarela; Mayank Varia; Mark Crovella; Dino P. Christenson

Paper 2025/1468

Privacy-Preserving Machine Learning on Web Browsing for Public Opinion

Sam Buxbaum, Boston University

Lucas M. Tassis, Boston University

Lucas Boschelli, Maritz

Giovanni Comarela, Universidade Federal do Espirito Santo

Mayank Varia, Boston University

Mark Crovella, Boston University

Dino P. Christenson, Washington University

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 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.

Metadata

Available format(s): PDF
Category: Applications
Publication info: Published elsewhere. Minor revision. CSCML 25
Keywords: MPC privacy-preserving machine learning deployment
Contact author(s): sambux @ bu edu
ltassis @ bu edu
boschellil @ wustl edu
gc @ inf ufes br
varia @ bu edu
crovella @ bu edu
dinopc @ wustl edu
History: 2025-08-13: approved; 2025-08-12: received; See all versions
Short URL: https://ia.cr/2025/1468
License: CC BY

BibTeX

@misc{cryptoeprint:2025/1468,
      author = {Sam Buxbaum and Lucas M. Tassis and Lucas Boschelli and Giovanni Comarela and Mayank Varia and Mark Crovella and Dino P. Christenson},
      title = {Privacy-Preserving Machine Learning on Web Browsing for Public Opinion},
      howpublished = {Cryptology {ePrint} Archive, Paper 2025/1468},
      year = {2025},
      url = {https://eprint.iacr.org/2025/1468}
}