Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

TL;DR

This paper develops a privacy-preserving dynamic pricing strategy that learns demand while protecting customer privacy using differential privacy, achieving near-optimal revenue guarantees in adversarial and stochastic settings.

Contribution

It introduces a novel notion of anticipating differential privacy tailored for dynamic pricing and provides algorithms with provable regret bounds under privacy constraints.

Findings

Achieves regret of O(\u03b5^{-1} ^{3/2} mp;^{1/2} mp;^{1/2} mp;^{1/2} mp;^{1/2} mp;^{1/2} T) in adversarial settings.

Improves regret to O( T^{1/2} + ^{2} mp;4) in stochastic settings.

Balances revenue maximization with privacy guarantees.

Abstract

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 \emph{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 \emph{anticipating} $(\varepsilon, \delta)$-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 $\tilde{O}(\varepsilon^{-1} \sqrt{d^3 T})$, when the customers' information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to $\tilde{O}(\sqrt{d^2T} + \varepsilon^{-2} d^2)$