Privacy-Preserving Personalized Revenue Management
Privacy-Preserving Personalized Revenue Management
复制标题
保护隐私的个性化收入管理
DOI：
10.2139/ssrn.3704446
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Ruslan Momot
中科院分区：
文献类型：
--
作者：
Y. Lei;Sentao Miao;Ruslan Momot
本文探讨了如何在保护消费者隐私的同时做出数据驱动的个性化决策。我们的设置是公司根据每个新客户的个人特征向量选择一个个性化的价格；个别需求产生参数的真实集合对企业来说是未知的，因此必须从历史数据中进行估计。我们扩展了这一经典的个性化定价框架，要求公司的定价政策保护消费者隐私，或者（正式地）要求它是差异化的隐私——这是隐私保护的行业标准。我们考虑的两种设置在理论上和实践上都是相关的：差异隐私的中心模型和局部模型，它们提供的隐私保证的强度不同。对于这两种模型，我们都开发了保护隐私的个性化定价算法，并推导了以公司收入衡量其性能的理论界限。我们的分析表明，如果公司拥有足够数量的历史数据，那么它可以以与由于估计误差导致的“经典”收入损失相同的成本来实现中心差异隐私。通过对两种模型的比较，我们得出结论：局部差异私有个性化定价能提供更好的隐私保障，但会导致企业更大的收益损失。我们在基于合成生成的和真实世界的在线自动借贷（CPRM-12-001）数据集的一系列数值实验中证实了我们的理论发现。最后，将本文的理论框架应用到个性化分类优化的设置中。
This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer's vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend this classical framework of personalized pricing by requiring also that the firm's pricing policy preserve consumer privacy, or (formally) that it be differentially private -- an industry standard for privacy preservation. The two settings we consider are theoretically and practically relevant: central and local models of differential privacy, which differ in the strength of the privacy guarantees they provide. For both models, we develop privacy-preserving personalized pricing algorithms and derive the theoretical bounds on their performance as measured by the firm's revenue. Our analyses suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve central differential privacy at a cost of the same order as the "classical" loss in revenue due to estimation error. Comparing the two models, we conclude that local differentially private personalized pricing yields better privacy guarantees but leads to much greater revenue loss by the firm. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and real-world On-line Auto Lending (CPRM-12-001) data sets. Finally, we also apply our theoretical framework to the setting of personalized assortment optimization.
DOI：
10.2139/ssrn.3127719
发表时间：
2018-10
期刊：
Operations Research eJournal
影响因子：
--
作者：
Adam N. Elmachtoub;Vishal Gupta;Michael L. Hamilton
通讯作者：
Adam N. Elmachtoub;Vishal Gupta;Michael L. Hamilton
DOI：
10.2139/ssrn.2579462
发表时间：
2015-03
期刊：
Revenue & Yield Management eJournal
影响因子：
--
作者：
Xi Chen;Zachary Owen;Clark Pixton;D. Simchi-Levi
通讯作者：
Xi Chen;Zachary Owen;Clark Pixton;D. Simchi-Levi
通过需求学习保护隐私的动态个性化定价
影响因子：
5.4
作者：
Chen, Xi;Simchi-Levi, David;Wang, Yining
通讯作者：
Wang, Yining