Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

Xi Chen (), David Simchi-Levi () and Yining Wang ()
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Xi Chen: Leonard N. Stern School of Business, New York University, New York, New York 10012
David Simchi-Levi: MIT Institute for Data, Systems, and Society, Department of Civil and Environmental Engineering, and Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Yining Wang: Warrington College of Business, University of Florida, Gainesville, Florida 32611

Management Science, 2022, vol. 68, issue 7, 4878-4898

Abstract: The prevalence of e-commerce 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 third-party 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 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 ( ε , δ ) -differential privacy that is tailored to the 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 O ˜ ( ε − 1 d 3 T ) when the customers’ information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to O ˜ ( d 2 T + ε − 2 d 2 ) .

Keywords: differential privacy (DP); dynamic pricing; generalized linear bandits; personal information (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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