Privacy-Preserving Dynamic Personalized Pricing with Demand Learning
<jats:p> 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...
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| Format: | Article |
| Language: | English |
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Institute for Operations Research and the Management Sciences (INFORMS)
2023
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| Online Access: | https://hdl.handle.net/1721.1/148657 |
| _version_ | 1826200588394692608 |
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| author | Chen, Xi Simchi-Levi, David Wang, Yining |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Chen, Xi Simchi-Levi, David Wang, Yining |
| author_sort | Chen, Xi |
| collection | MIT |
| description | <jats:p> 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 [Formula: see text]-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 [Formula: see text] when the customers’ information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to [Formula: see text]. </jats:p><jats:p> This paper was accepted by J. George Shanthikumar, big data analytics. </jats:p> |
| first_indexed | 2024-09-23T11:38:43Z |
| format | Article |
| id | mit-1721.1/148657 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:38:43Z |
| publishDate | 2023 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1486572023-03-22T03:32:23Z Privacy-Preserving Dynamic Personalized Pricing with Demand Learning Chen, Xi Simchi-Levi, David Wang, Yining Massachusetts Institute of Technology. Department of Civil and Environmental Engineering <jats:p> 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 [Formula: see text]-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 [Formula: see text] when the customers’ information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to [Formula: see text]. </jats:p><jats:p> This paper was accepted by J. George Shanthikumar, big data analytics. </jats:p> 2023-03-21T17:25:57Z 2023-03-21T17:25:57Z 2022 2023-03-21T17:20:31Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/148657 Chen, Xi, Simchi-Levi, David and Wang, Yining. 2022. "Privacy-Preserving Dynamic Personalized Pricing with Demand Learning." Management Science, 68 (7). en 10.1287/MNSC.2021.4129 Management Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) SSRN |
| spellingShingle | Chen, Xi Simchi-Levi, David Wang, Yining Privacy-Preserving Dynamic Personalized Pricing with Demand Learning |
| title | Privacy-Preserving Dynamic Personalized Pricing with Demand Learning |
| title_full | Privacy-Preserving Dynamic Personalized Pricing with Demand Learning |
| title_fullStr | Privacy-Preserving Dynamic Personalized Pricing with Demand Learning |
| title_full_unstemmed | Privacy-Preserving Dynamic Personalized Pricing with Demand Learning |
| title_short | Privacy-Preserving Dynamic Personalized Pricing with Demand Learning |
| title_sort | privacy preserving dynamic personalized pricing with demand learning |
| url | https://hdl.handle.net/1721.1/148657 |
| work_keys_str_mv | AT chenxi privacypreservingdynamicpersonalizedpricingwithdemandlearning AT simchilevidavid privacypreservingdynamicpersonalizedpricingwithdemandlearning AT wangyining privacypreservingdynamicpersonalizedpricingwithdemandlearning |