# Privacy-Preserving Dynamic Assortment Selection

arXiv: https://arxiv.org/abs/2410.22488 · HTML: https://mlsanity.com/paper/2410.22488/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 51 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [Composition of Differential Privacy & Privacy Amplification by Subsampling](https://mlsanity.com/paper/2210.00597/) (2022)
- [Provably Optimal Algorithms for Generalized Linear Contextual Bandits](https://mlsanity.com/paper/1703.00048/) (2017)
- [Thompson Sampling for the MNL-Bandit](https://mlsanity.com/paper/1706.00977/) (2017)
- [Generalized Linear Bandits with Local Differential Privacy](https://mlsanity.com/paper/2106.03365/) (2021)
- [Nearly Minimax Optimal Regret for Multinomial Logistic Bandit](https://mlsanity.com/paper/2405.09831/) (2024)
- [Multinomial Logit Bandit with Linear Utility Functions](https://mlsanity.com/paper/1805.02971/) (2018)
- [Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners](https://mlsanity.com/paper/2401.00583/) (2023)
- [Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited](https://mlsanity.com/paper/2303.18047/) (2023)
- [Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization](https://mlsanity.com/paper/2301.02220/) (2023)
- [PASTA: Pessimistic Assortment Optimization](https://mlsanity.com/paper/2302.03821/) (2023)
- [Low-Rank Online Dynamic Assortment with Dual Contextual Information](https://mlsanity.com/paper/2404.17592/) (2024)
- [Rate-Optimal Rank Aggregation with Private Pairwise Rankings](https://mlsanity.com/paper/2402.16792/) (2024)

## Abstract (arXiv)

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 of tildeO(√T) 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.