Privacy-Preserving Dynamic Assortment Selection

Young Hyun Cho, Will Wei Sun

Abstract:

With the growing demand for personalized assortment recommendations, concerns over data
privacy have intensified, highlighting the urgent need for effective privacy-preserving strategies.
This paper presents a novel framework for privacy-preserving dynamic assortment selection
using the multinomial logit (MNL) bandits model. Our approach employs a perturbed
upper confidence bound method, integrating calibrated noise into user utility estimates to
balance between exploration and exploitation while ensuring robust privacy protection. We
rigorously prove that our policy satisfies Joint Differential Privacy (JDP), which better suits
dynamic environments than traditional differential privacy, effectively mitigating inference
attack risks. This analysis is built upon a novel objective perturbation technique tailored for
MNL bandits, which is also of independent interest. Theoretically, we derive a near-optimal
regret bound for our policy and explicitly quantify how privacy protection impacts
regret. Through extensive simulations and an application to the Expedia hotel dataset, we
demonstrate substantial performance enhancements over the benchmark method.