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Creators/Authors contains: "Christenson, Dino P"

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  1. ; ; ; ; ; ; (, Springer)
    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 using 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 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. 
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    Free, publicly-accessible full text available December 4, 2026
  2. ; ; ; ; ; ; (, Journal of Computational and Graphical Statistics)
    Free, publicly-accessible full text available February 27, 2026
  3. ; ; ; ; ; (, Journal of the Royal Statistical Society Series A: Statistics in Society)
    Abstract We develop a broadly applicable class of coevolving latent space network with attractors (CLSNA) models, where nodes represent individual social actors assumed to lie in an unknown latent space, edges represent the presence of a specified interaction between actors, and attractors are added in the latent level to capture the notion of attractive and repulsive forces. We apply the CLSNA models to understand the dynamics of partisan polarization in US politics on social media, where we expect Republicans and Democrats to increasingly interact with their own party and disengage with the opposing party. Using longitudinal social networks from the social media platforms Twitter and Reddit, we quantify the relative contributions of positive (attractive) and negative (repulsive) forces among political elites and the public, respectively. 
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