The prevalence of ecommerce has made customers’ detailed personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When using personalized information, the question of 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 thirdparty agent might infer the personalized information and purchase decisions from price changes in the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacypreserving 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 [Formula: see text]differential privacy that is tailored to the dynamic pricing problem. Our policy achieves both the privacy guarantee and the performancemore »
This content will become publicly available on August 1, 2023
Differential Privacy in Personalized Pricing with Nonparametric Demand Models
In recent decades, the advance of information technology and abundant personal data facilitate the application of algorithmic personalized pricing. However, this leads to the growing concern of potential violation of privacy because of adversarial attack. To address the privacy issue, this paper studies a dynamic personalized pricing problem with unknown nonparametric demand models under data privacy protection. Two concepts of data privacy, which have been widely applied in practices, are introduced: central differential privacy (CDP) and local differential privacy (LDP), which is proved to be stronger than CDP in many cases. We develop two algorithms that make pricing decisions and learn the unknown demand on the fly while satisfying the CDP and LDP guarantee, respectively. In particular, for the algorithm with CDP guarantee, the regret is proved to be at most [Formula: see text]. Here, the parameter T denotes the length of the time horizon, d is the dimension of the personalized information vector, and the key parameter [Formula: see text] measures the strength of privacy (smaller ε indicates a stronger privacy protection). Conversely, for the algorithm with LDP guarantee, its regret is proved to be at most [Formula: see text], which is near optimal as we prove a lower more »
 Award ID(s):
 1845444
 Publication Date:
 NSFPAR ID:
 10376128
 Journal Name:
 Operations Research
 ISSN:
 0030364X
 Sponsoring Org:
 National Science Foundation
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