# Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

arXiv: https://arxiv.org/abs/2009.12920 · HTML: https://mlsanity.com/paper/2009.12920/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

## Built on by (most cited first, 23 of 23)

- [Generalized Linear Bandits with Local Differential Privacy](https://mlsanity.com/paper/2106.03365/) (2021)
- [Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms](https://mlsanity.com/paper/2201.03968/) (2022)
- [Policy Optimization Using Semi-parametric Models for Dynamic Pricing](https://mlsanity.com/paper/2109.06368/) (2021)
- [Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update](https://mlsanity.com/paper/2507.11847/) (2025)
- [Differential Privacy in Personalized Pricing with Nonparametric Demand Models](https://mlsanity.com/paper/2109.04615/) (2021)
- [Fairness-aware Online Price Discrimination with Nonparametric Demand Models](https://mlsanity.com/paper/2111.08221/) (2021)
- [On Private and Robust Bandits](https://mlsanity.com/paper/2302.02526/) (2023)
- [Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data](https://mlsanity.com/paper/2406.13130/) (2024)
- [Utility Fairness in Contextual Dynamic Pricing with Demand Learning](https://mlsanity.com/paper/2311.16528/) (2023)
- [Differential Privacy via Distributionally Robust Optimization](https://mlsanity.com/paper/2304.12681/) (2023)
- [On the Optimal Regret of Locally Private Linear Contextual Bandit](https://mlsanity.com/paper/2404.09413/) (2024)
- [Multi-Task Dynamic Pricing in Credit Market with Contextual Information](https://mlsanity.com/paper/2410.14839/) (2024)
- [Optimal Contextual Pricing under Agnostic Non-Lipschitz Demand](https://mlsanity.com/paper/2605.05609/) (2026)
- [On Private Online Convex Optimization: Optimal Algorithms in ℓp-Geometry and High Dimensional Contextual Bandits](https://mlsanity.com/paper/2206.08111/) (2022)
- [Majority Vote for Distributed Differentially Private Sign Selection](https://mlsanity.com/paper/2209.04419/) (2022)
- [Privacy Preserving Adaptive Experiment Design](https://mlsanity.com/paper/2401.08224/) (2024)
- [From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models](https://mlsanity.com/paper/2411.17126/) (2024)
- [Minimax PAC Bounds for Learning in Exogenous Contextual MDPs](https://mlsanity.com/paper/2606.25170/) (2026)
- [Phase Transitions in Learning and Earning under Price Protection Guarantee](https://mlsanity.com/paper/2211.01798/) (2022)
- [Differentially Private High Dimensional Bandits](https://mlsanity.com/paper/2402.03737/) (2024)
- [Private Optimal Inventory Policy Learning for Feature-based Newsvendor with Unknown Demand](https://mlsanity.com/paper/2404.15466/) (2024)
- [Beyond ATE: Multi-Criteria Design for A/B Testing](https://mlsanity.com/paper/2509.05864/) (2025)
- [Privacy Preserving Reinforcement Learning with One-Sided Feedback](https://mlsanity.com/paper/2605.18246/) (2026)

## Abstract (arXiv)

The prevalence of e-commerce has made detailed customers' personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When involving personalized information, how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over T time periods with an unknown demand function of posted price and personalized information. At each time t, the retailer observes an arriving customer's personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third party agent might infer the personalized information and purchase decisions from price changes from the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer's information and purchasing decisions. To this end, we first introduce a notion of anticipating (varepsilon, δ)-differential privacy that is tailored to dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for d-dimensional personalized information, our algorithm achieves the expected regret at the order of tildeO(varepsilon-1 √d3 T), when the customers' information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to tildeO(√d2T + varepsilon-2 d2)