Differential Privacy in Personalized Pricing with Nonparametric Demand Models
References
- (2016) The economics of privacy. J. Econom. Literature 54(2):442–492.Crossref, Google Scholar
- (2013). On maximal correlation, hypercontractivity, and the data processing inequality studied by Erkip and Cover. Preprint, submitted April 22, https://arxiv.org/abs/1304.6133.Google Scholar
- Apple (2019) Improving Siri’s privacy protections. Accessed April 9, 2022, https://www.apple.com/ca/newsroom/2019/08/improving-siris-privacy-protections/.Google Scholar
- (2009) Dynamic pricing for nonperishable products with demand learning. Oper. Res. 57(5):1169–1188.Link, Google Scholar
- (2019) A survey on driving behavior analysis in usage based insurance using big data. J. Big Data 6(1):1–21.Crossref, Google Scholar
- (1983) Deux remarques sur l’estimation. Comptes rendus des séances de l’Académie des sciences. Série 1. Mathématique 296(23):1021–1024.Google Scholar
- (2021) Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity. Management Sci. 67(9):5549–5568.Link, Google Scholar
- (2009) Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms. Oper. Res. 57(6):1407–1420.Link, Google Scholar
- (2015) On the (surprising) sufficiency of linear models for dynamic pricing with demand learning. Management Sci. 61(4):723–739.Link, Google Scholar
- (2021). Data tracking under competition. Preprint, submitted February 10, https://dx.doi.org/10.2139/ssrn.3808228.Google Scholar
- (2021a). Markdown policies for demand learning with forward-looking customers. Preprint, submitted December 6, https://dx.doi.org/10.2139/ssrn.3299819.Google Scholar
- (2021b) Dynamic learning and market making in spread betting markets with informed bettors. Oper. Res. 69(6):1746–1766.Link, Google Scholar
- (2012) Dynamic pricing under a general parametric choice model. Oper. Res. 60(4):965–980.Link, Google Scholar
- (2020). The cost of privacy in generalized linear models: Algorithms and minimax lower bounds. Preprint, submitted December 6, https://arxiv.org/abs/2011.03900.Google Scholar
- (2021) The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy. Ann. Statist. 49(5):2825–2850.Crossref, Google Scholar
- (2011) Private and continual release of statistics. ACM Trans. Inform. Systems Security 14(3):1–24.Crossref, Google Scholar
- (2021) Nonparametric pricing analytics with customer covariates. Oper. Res. 69(3):974–984.Link, Google Scholar
- (2015) Real-time dynamic pricing with minimal and flexible price adjustment. Management Sci. 62(8):2437–2455.Link, Google Scholar
- (2022) Privacy-preserving dynamic personalized pricing with demand learning. Management Sci. 68(7):4878–4898.Google Scholar
- (2017) Dynamic pricing and demand learning with limited price experimentation. Oper. Res. 65(6):1722–1731.Link, Google Scholar
- (2015) Modern Principles of Economics (Macmillan International Higher Education).Google Scholar
- (2015) From Cradle to Cane: The Cost of Being a Female Consumer. A Study of Gender Pricing in New York City (The New York Department of Consumer Affairs, New York).Google Scholar
- (2020) Discontinuous demand functions: Estimation and pricing. Management Sci. 66(10):4516–4534.Link, Google Scholar
- (2013) Simultaneously learning and optimizing using controlled variance pricing. Management Sci. 60(3):770–783.Link, Google Scholar
- (2013) Local privacy and statistical minimax rates. Proc. IEEE Annual Sympos. on Foundations of Comput. Sci. (IEEE, New York), 429–438.Google Scholar
- (2018) Minimax optimal procedures for locally private estimation. J. Amer. Statist. Assoc. 113(521):182–201.Crossref, Google Scholar
- (2014) The algorithmic foundations of differential privacy. Foundations Trends Theoretical Comput. Sci. 9(3-4):211–407.Crossref, Google Scholar
- (2006b) Calibrating noise to sensitivity in private data analysis. Proc. Theory of Cryptography Conf. (Springer, Berlin), 265–284.Crossref, Google Scholar
- (2014). Analyze Gauss: Optimal bounds for privacy-preserving principal component analysis. Proc. Annual ACM Sympos. on Theory of Comput. (ACM, New York), 11–20).Google Scholar
- (2006a) Our data, ourselves: Privacy via distributed noise generation. Proc. Annual Internat. Conf. on the Theory and Applications of Cryptographic Techniques (Springer, Berlin), 486–503.Google Scholar
- (2015) Robust traceability from trace amounts. Proc. IEEE 56th Annual Sympos. on Foundations of Comput. Sci. (IEEE, New York), 650–669.Google Scholar
- (2003) Limiting privacy breaches in privacy preserving data mining. Proc. ACM SIGMOD-SIGACT-SIGART Sympos. on Principles of Database Systems (ACM, New York), 211–222.Crossref, Google Scholar
- (1993) Nonparametric regression with errors in variables. Ann. Statist. 21(1):1900–1925.Crossref, Google Scholar
- (2010) Dynamic pricing with a prior on market response. Oper. Res. 58(1):16–29.Link, Google Scholar
- Federal Trade Commission (2012) Protecting consumer privacy in an era of rapid change. https://bit.ly/3k2AUhN.Google Scholar
- (2018) Online network revenue management using thompson sampling. Oper. Res. 66(6):1586–1602.Link, Google Scholar
- (2014). Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing. Proc. 23rd USENIX Security Sympos. (USENIX Association, San Diego), 17–32.Google Scholar
- Google (2014) Learning statistics with privacy, aided by the flip of a coin. Accessed April 9, 2022, https://ai.googleblog.com/2014/10/learning-statistics-with-privacy-aided.html.Google Scholar
- (2021). Generalized linear bandits with local differential privacy. Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Wortman Vaughan J, eds. Advances in Neural Information Processing Systems, vol. 34 (Curran Associates, Inc., Red Hook, NY), 26511–26522.Google Scholar
- (2012) Bayesian dynamic pricing policies: Learning and earning under a binary prior distribution. Management Sci. 58(3):570–586.Link, Google Scholar
- (2017) Model inversion attacks for prediction systems: Without knowledge of non-sensitive attributes. Proc. 15th Annual Conf. on Privacy, Security and Trust (IEEE, New York), 115–11509.Google Scholar
- (1963) Probability inequalities for sums of bounded random variables. J. Amer. Statist. Assoc. 58(301):13–30.Crossref, Google Scholar
- (2022) Privacy management in service systems. Manufacturing Service Oper. Management, ePub ahead of print July 22, https://doi.org/10.1287/msom.2022.1130.Google Scholar
- (2019) Dynamic pricing in high-dimensions. J. Machine Learn. Res. 20(9):1–49.Google Scholar
- (2014) Dynamic pricing with an unknown demand model: Asymptotically optimal semi-myopic policies. Oper. Res. 62(5):1142–1167.Link, Google Scholar
- (2018) On incomplete learning and certainty-equivalence control. Oper. Res. 66(4):1136–1167.Link, Google Scholar
- (2020) Data-driven clustering and feature-based retail electricity pricing with smart meters. Preprint, submitted March 7, https://dx.doi.org/10.2139/ssrn.3686518.Google Scholar
- (2019) Your data were ‘anonymized’? These scientists can still identify you. New York Times.Google Scholar
- (2014) Near-optimal bisection search for nonparametric dynamic pricing with inventory constraint. Preprint, submitted September 20, https://dx.doi.org/10.2139/ssrn.2509425.Google Scholar
- (2020) Privacy-preserving personalized revenue management. Preprint, submitted May 9, https://dx.doi.org/10.2139/ssrn.3704446.Google Scholar
- (2021) A general framework for resource constrained revenue management with demand learning and large action space. Preprint, submitted September 16, https://dx.doi.org/10.2139/ssrn.3841273.Google Scholar
- (2015) (Nearly) optimal differentially private stochastic multi-arm bandits. Proc. Conf. on Uncertainty in Artificial Intelligence (AUAI Press, Arlington, VA), 592–601.Google Scholar
- (2016) Dynamic pricing with demand covariates. Preprint, submitted April 25, https://dx.doi.org/10.2139/ssrn.2765257.Google Scholar
- (2020) Multi-armed bandits with local differential privacy. Preprint, submitted July 6, https://arxiv.org/abs/2007.03121.Google Scholar
- (2018) Differentially private contextual linear bandits. Advances in Neural Information Processing Systems (NeurIPS) (Curran Associates, Inc., Red Hook, NY).Google Scholar
- (2020). Differentially private contextual dynamic pricing. In Proceedings of the International Conference on Autonomous Agents and MultiAgent Systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC), 1368–1376.Google Scholar
- Tsitsiklis JN, Xu K, Xu Z (2021) Private sequential learning. Oper. Res. 69(5):1575–1590.Google Scholar
- U.S. Census Bureau (2020) 2020 census data products: Disclosure avoidance modernization. Accessed April 9, 2022, https://www.census.gov/programs-surveys/decennial-census/decade/2020/planning-management/process/disclosure-avoidance.html.Google Scholar
- (2014) Crying wolf? On the price discrimination of online airline tickets. Proc. 7th Workshop on Hot Topics in Privacy Enhancing Technologies (Amsterdam).Google Scholar
- (2014) Close the gaps: A learning-while-doing algorithm for single-product revenue management problems. Oper. Res. 62(2):219–482.Link, Google Scholar
- (2021) Uncertainty quantification for demand prediction in contextual dynamic pricing. Production Oper. Management 30(6):1703–1717.Crossref, Google Scholar
- (2018) Query complexity of Bayesian private learning. Proc. Adv. in Neural Inform. Processing Systems (Curran Associates, Inc., Red Hook, NY).Google Scholar
- (2021) Optimal query complexity for private sequential learning against eavesdropping. Banerjee A, Fukumizu K, eds. Proc. 24th Internat. Conf. Artificial Intelligence Statist., Proceedings of Machine Learning Research Series (PMLR), 2296–2304.Google Scholar
- (1997) Assouad, Fano, and le Cam. Festschrift for Lucien Le Cam (Springer, Berlin), 423–435.Crossref, Google Scholar
- (2020) Locally differentially private (contextual) bandits learning. Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, eds. Advances in Neural Information Processing Systems (Curran Associates, Inc., Red Hook, NY), 12300–12310.Google Scholar

