Privacy-Preserving Predictive Model Using Factor Analysis for Neuroscience Applications

摘要

The purpose of this article is to present an algorithm which maximizes prediction accuracy under a linear regression model while preserving data privacy. This approach anonymizes the data such that the privacy of the original features is fully guaranteed, and the deterioration in predictive accuracy using the anonymized data is minimal. The proposed algorithm employs two stages: the first stage uses a probabilistic latent factor approach to anonymize the original features into a collection of lower dimensional latent factors, while the second stage uses an optimization algorithm to tune the anonymized data further, in a way which ensures a minimal loss in prediction accuracy under the predictive approach specified by the user. We demonstrate the advantages of our approach via numerical studies and apply our method to high-dimensional neuroimaging data where the goal is to predict the behavior of adolescents and teenagers based on functional magnetic resonance imaging (fMRI) measurements.