Design Preference Prediction With Data Privacy Safeguards: A Preliminary Study

Abstract

Design preference models are used widely in product planning and design development. Their prediction accuracy requires large amounts of personal user data including purchase and other personal choice records. With increased Internet and smart device use, sources of personal data are becoming more varied and their capture more ubiquitous. This situation leads to questioning whether there is a trade off between improving products and compromising individual user privacy. To advance this conversation, we analyze how privacy safeguards may affect design preference modeling. We conduct an experiment using real user data to study the performance of design preference models under different levels of privacy. Results indicate there is a tradeoff between accuracy and privacy. However, with enough data, models with privacy safeguards can still be sufficiently accurate to answer population-level design questions.