Homomorphic Encrypted Revenue Management

54 Pages Posted: 12 Mar 2024 Last revised: 5 Apr 2024

See all articles by Mojtaba Abdolmaleki

Mojtaba Abdolmaleki

University of Michigan, Stephen M. Ross School of Business

Ruslan Momot

University of Michigan, Stephen M. Ross School of Business

Date Written: February 13, 2024

Abstract

We develop a novel homomorphic encryption-based approach to privacy preservation in a dynamic personalized pricing setting. In each period, the firm offers a personalized price to an incoming customer based on (i) this customer's observable characteristics and (ii) the firm's estimate of the demand function (obtained using the data of the historical customers with whom the firm interacted in the past). Our method enables the firm to use homomorphic encryption to encrypt the data of incoming and historical customers, then estimate the demand function and personalize prices directly based on these encrypted data without the need to decrypt them. In contrast to the previous literature, which only preserves the privacy of historical customers via adding statistical noise to their data (so-called statistics-based approach), our approach allows the firm to protect the privacy of all customers -- both incoming and historical. Our theoretical analysis further reveals that our approach i) provides perfect privacy protection (achieving 0-differential privacy) and ii) does so at no cost to the firm's expected revenue, thus achieving better revenue performance than statistics-based algorithms, but (iii) it is computationally expensive. We thus develop a hybrid approach to privacy preservation that leverages the strengths of both statistics- and encryption-based methods, achieving the required privacy protection at a comparatively lower computational cost without significant compromise on the expected revenue. We confirm our theoretical findings through a numerical example based on synthetically generated data.

Keywords: privacy preservation, homomorphic encryption, personalized revenue management, personalized pricing

JEL Classification: C40, C44, C61, M20, M30

Suggested Citation

Abdolmaleki, Mojtaba and Momot, Ruslan, Homomorphic Encrypted Revenue Management (February 13, 2024). Available at SSRN: https://ssrn.com/abstract=4724820 or http://dx.doi.org/10.2139/ssrn.4724820

Mojtaba Abdolmaleki

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States
7348815989 (Phone)

Ruslan Momot (Contact Author)

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States

HOME PAGE: http://www.ruslanmomot.info

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