Privacy-Preserving Personalized Revenue Management

Published Online:https://doi.org/10.1287/mnsc.2023.4925

References

  • Accenture (2018) Making it personal: Why brands must move from communication to conversation for greater personalization. https://tinyurl.com/4hbd746m.Google Scholar
  • Acemoglu D, Makhdoumi A, Malekian A, Ozdaglar A (2017) Privacy-constrained network formation. Games Econom. Behav. 105:255–275.CrossrefGoogle Scholar
  • Acemoglu D, Makhdoumi A, Malekian A, Ozdaglar A (2022) Too much data: Prices and inefficiencies in data markets. Am. Econm. J. Microecon. 14(4):218–256.Google Scholar
  • Acquisti A, Taylor C, Wagman L (2016) The economics of privacy. J. Econom. Literature 54(2):442–492.CrossrefGoogle Scholar
  • Ahuja RK, Orlin JB (2001) Inverse optimization. Oper. Res. 49(5):771–783.LinkGoogle Scholar
  • Andrade-Walz (2020) Privacy is the new competitive battleground. TechCrunch (December 16), https://tinyurl.com/msztpzhz.Google Scholar
  • Apple (2019) Improving Siri’s privacy protections. Accessed September 7, 2023, https://apple.co/3GwnLvj.Google Scholar
  • Argenziano R, Bonatti A (2020) Information revelation and privacy protection. Working paper. University of Essex, Essex, UK.Google Scholar
  • Atallah M, Blanton M, Deshpande V, Frikken K, Li J, Schwarz L (2006) Secure collaborative planning, forecasting, and replenishment. Proc. Multi-Echelon/Public Appl. of Supply Chain Management Conf., 165–180.Google Scholar
  • Auxier B, Rainie L, Anderson M, Perrin A, Kumar M, Turner E (2019) Americans and privacy: Concerned, confused and feeling lack of control over their personal information. PEW Research Center (November 15), https://tinyurl.com/2s3vr565.Google Scholar
  • Awad NF, Krishnan MS (2006) The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization. Management Inform. Systems Quart. (2006):13–28.CrossrefGoogle Scholar
  • Aydin G, Ziya S (2009) Personalized dynamic pricing of limited inventories. Oper. Res. 57(6):1523–1531.LinkGoogle Scholar
  • Aziz A (2020) The power of purpose: How lemonade is disrupting insurance with goodness (and a new foundation). Forbes (March 9), https://tinyurl.com/58wwu66x.Google Scholar
  • Baardman L, Boroujeni SB, Cohen-Hillel T, Panchamgam K, Perakis G (2020) Detecting customer trends for optimal promotion targeting. Manufacturing Services Oper. Management 25(2):448–467.Google Scholar
  • Ban GY, Keskin NB (2020) Personalized dynamic pricing with machine learning: High dimensional features and heterogeneous elasticity. Management Sci. 67(9):5549–5568.Google Scholar
  • Bassily R, Smith A, Thakurta A (2014) Private empirical risk minimization: Efficient algorithms and tight error bounds. Proc. IEEE 55th Annual Sympos. on Foundations of Comput. Sci. (IEEE, Piscataway, NJ), 464–473.Google Scholar
  • Bastani H, Bayati M (2020) Online decision making with high-dimensional covariates. Oper. Res. 68(1):276.LinkGoogle Scholar
  • Bastani H, Simchi-Levi D, Zhu R (2021) Meta dynamic pricing: Transfer learning across experiments. Management Sci. 68(3):1865–1881.LinkGoogle Scholar
  • Bergemann D, Bonatti A, Gan T (2022) The economics of social data. RAND J. Econ. 53(2):263–296.Google Scholar
  • Bernstein F, Kök AG, Xie L (2015) Dynamic assortment customization with limited inventories. Manufacturing Services Oper. Management 17(4):538–553.LinkGoogle Scholar
  • Bernstein F, Modaresi S, Sauré D (2019) A dynamic clustering approach to data-driven assortment personalization. Management Sci. 65(5):2095–2115.AbstractGoogle Scholar
  • Bimpikis K, Crapis D, Tahbaz-Salehi A (2019) Information sale and competition. Management Sci. 65(6):2646–2664.LinkGoogle Scholar
  • Bimpikis K, Morgenstern I, Saban D (2023) Data tracking under competition. Oper. Res. Forthcoming.Google Scholar
  • Bimpikis K, Ozdaglar A, Yildiz E (2016) Competitive targeted advertising over networks. Oper. Res. 64(3):705–720.LinkGoogle Scholar
  • Cai TT, Wang Y, Zhang L (2021) The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy. Ann. Stat. 49(5):2825–2850.Google Scholar
  • Candogan O, Drakopoulos K (2020) Optimal signaling of content accuracy: Engagement vs. misinformation. Oper. Res. 68(2):497–515.AbstractGoogle Scholar
  • Chan TC, Mahmood R, Zhu IY (2021) Inverse optimization: Theory and applications. Preprint, submitted September 8, https://arxiv.org/abs/2109.03920.Google Scholar
  • Chen N, Gallego G (2020) Nonparametric pricing analytics with customer covariates. Oper. Res. 69(3):974–984.Google Scholar
  • Chen X, Miao S, Wang Y (2022) Differential privacy in personalized pricing with nonparametric demand models. Oper. Res. 71(2):581–602.Google Scholar
  • Chen X, Simchi-Levi D, Wang Y (2021a) Privacy-preserving dynamic personalized pricing with demand learning. Management Sci. 68(7):4878–4898.Google Scholar
  • Chen X, Owen Z, Pixton C, Simchi-Levi D (2021b) A statistical learning approach to personalization in revenue management. Management Sci. 68(3):1923–1937.Google Scholar
  • Cheung WC, Simchi-Levi D, Zhu R (2022) Hedging the drift: Learning to optimize under non-stationarity. Management Sci. 68(3):1696–1713.Google Scholar
  • Cohen M, Elmachtoub AN, Lei X (2022) Price discrimination with fairness constraints. Management Sci. 68(12):8536–8552.Google Scholar
  • Cohen MC, Lobel I, Paes Leme R (2020) Feature-based dynamic pricing. Management Sci. 66(11):4921–4943.Google Scholar
  • Coulter M, Shubber L (2019) Equifax to pay almost $800m in US settlement over data breach. Financial Times (July 22), https://tinyurl.com/3a3efuw2.Google Scholar
  • Deloitte (2017) Responding to cyber threats: A change in paradigm. https://bit.ly/3WrzGPM.Google Scholar
  • Deloitte (2019) Deloitte consumer privacy in retail: The next regulatory and competitive frontier. https://tinyurl.com/2s4xxzet.Google Scholar
  • den Boer AV (2015) Dynamic pricing and learning: Historical origins, current research, and new directions. Survey Oper. Res. Management Sci. 20(1):1–18.CrossrefGoogle Scholar
  • Deshpande V, Schwarz LB, Atallah MJ, Blanton M, Frikken KB (2011) Outsourcing manufacturing: Secure price-masking mechanisms for purchasing component parts. Production Oper. Management 20(2):165–180.CrossrefGoogle Scholar
  • Drakopoulos K, Makhdoumi A (2023) Providing data samples for free. Management Sci. 69(6):3536–3560.LinkGoogle Scholar
  • Duchi JC, Jordan MI, Wainwright MJ (2013) Local privacy and statistical minimax rates. Proc. IEEE 54th Annual Sympos. on Foundations of Comput. Sci. (IEEE, Piscataway, NJ), 429–438.Google Scholar
  • Dwork C, Roth A, et al. (2014) The algorithmic foundations of differential privacy. Foundations Trends Theoretical Comput. Sci. 9(3–4):211–407.CrossrefGoogle Scholar
  • Elmachtoub AN, Gupta V, Hamilton M (2020) The value of personalized pricing. Management Sci. 67(10):6055–6070.Google Scholar
  • Erlingsson (2014) Learning statistics with privacy, aided by the flip of a coin. Google (October 30), https://tinyurl.com/3j8kvcn5.Google Scholar
  • Ettl M, Harsha P, Papush A, Perakis G (2020) A data-driven approach to personalized bundle pricing and recommendation. Manufacturing Service Oper. Management 22(3):461–480.LinkGoogle Scholar
  • Fainmesser IP, Galeotti A (2016) Pricing network effects. Rev. Econom. Stud. 83(1):165–198.CrossrefGoogle Scholar
  • Fainmesser IP, Galeotti A, Momot R (2023) Digital privacy. Management Sci. 69(6):3157–3173.Google Scholar
  • Fallah A, Makhdoumi A, Malekian A, Ozdaglar A (2022) Optimal and differentially private data acquisition: Central and local mechanisms. Preprint, submitted January 10, https://arxiv.org/abs/2201.03968.Google Scholar
  • Fredrikson M, Lantz E, Jha S, Lin S, Page D, Ristenpart T (2014) Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing. Proc. 23rd USENIX Security Sympos. (ACM, New York), 17–32.Google Scholar
  • Funk M (2019) How ice picks its targets in the surveillance age. The New York Times (October 2), https://tinyurl.com/343u6sbd.Google Scholar
  • Gallego G, Topaloglu H (2019) Revenue Management and Pricing Analytics, vol. 209 (Springer, Berlin).CrossrefGoogle Scholar
  • Golrezaei N, Nazerzadeh H, Rusmevichientong P (2014) Real-time optimization of personalized assortments. Management Sci. 60(6):1532–1551.LinkGoogle Scholar
  • Hidano S, Murakami T, Katsumata S, Kiyomoto S, Hanaoka G (2017) Model inversion attacks for prediction systems: Without knowledge of non-sensitive attributes. Proc. 15th Annual Conf. on Privacy, Security and Trust (IEEE, Piscataway, NJ), 115–11509.Google Scholar
  • Holohan N, Antonatos S, Braghin S, Mac Aonghusa P (2018) The bounded laplace mechanism in differential privacy. Preprint, submitted August 30, https://arxiv.org/abs/1808.10410.Google Scholar
  • Hu M, Momot R, Wang J (2022) Privacy management in service systems. Manufacturing Services Oper. Management 24(5):2761–2779.Google Scholar
  • Huang J, Mani A, Wang Z (2019) The value of price discrimination in large random networks. Proc. ACM Conf. on Econom. and Comput. (ACM, New York), 243–244.Google Scholar
  • Ichihashi S (2020) Online privacy and information disclosure by consumers. Amer. Econom. Rev. 110(2):569–595.CrossrefGoogle Scholar
  • Javanmard A, Nazerzadeh H (2019) Dynamic pricing in high-dimensions. J. Machine Learn. Res. 20(1):315–363.Google Scholar
  • Keskin NB, Zeevi A (2014) Dynamic pricing with an unknown demand model: Asymptotically optimal semi-myopic policies. Oper. Res. 62(5):1142–1167.LinkGoogle Scholar
  • KPMG (2021) Corporate data responsibility: Bridging the consumer trust gap. KPMG, Amstelveen, Netherlands.Google Scholar
  • Kumar RSS, Nyström M, Lambert J, Marshall A, Goertzel M, Comissoneru A, Swann M, et al. (2020) Adversarial machine learning-industry perspectives. Proc. IEEE Security and Privacy Workshops (IEEE, Piscataway, NJ), 69–75.Google Scholar
  • Manchiraju C, Dawande M, Janakiraman G (2022) Multiproduct pricing with discrete price sets. Oper. Res. 70(4):2185–2193.LinkGoogle Scholar
  • McGee P, Murphy H, Bradshaw T (2020) Coronavirus apps: The risk of slipping into a surveillance state. Financial Times (April 28), https://tinyurl.com/5af244xz.Google Scholar
  • McMillan Rn (2017) Apple expands bet on cutting edge privacy technology. Wall Street Journal (July 7), https://tinyurl.com/yn9cj2p6.Google Scholar
  • Mišić VV, Perakis G (2020) Data analytics in operations management: A review. Manufacturing Services Oper. Management 22(1):158–169.LinkGoogle Scholar
  • Momot R, Belavina E, Girotra K (2020) The use and value of social information in selective selling of exclusive products. Management Sci. 66(6):2610–2627.LinkGoogle Scholar
  • Morrison S (2021) A disturbing, viral twitter thread reveals how ai-powered insurance can go wrong. Vox (May 27), https://tinyurl.com/2wx7262n.Google Scholar
  • Mostagir M, Ozdaglar AE, Siderius J (2022) When is society susceptible to manipulation? Management Sci. 68(10):7153–7175.Google Scholar
  • Pedersen K, Sadler G, Smart V (2017) How companies use personal data to charge different people different prices for the same product. CBC News (November 24), https://tinyurl.com/msbcu6cf.Google Scholar
  • Perlroth N, Tsang A, Satariano A (2018) Marriott hacking exposes data of up to 500 million guests. The New York Times (November 30), https://www.nytimes.com/2018/11/30/business/marriott-data-breach.html.Google Scholar
  • Petro G (2019) Retailers walking a tightrope between data privacy and personalization. Forbes (May 17), https://tinyurl.com/2v4k8dcn.Google Scholar
  • Phillips R (2013) Optimizing prices for consumer credit. J. Revenue Pricing Managment 12(4):360–377.CrossrefGoogle Scholar
  • Pibernik R, Zhang Y, Kerschbaum F, Schröpfer A (2011) Secure collaborative supply chain planning and inverse optimization: The JELS model. Eur. J. Oper. Res. 208(1):75–85.CrossrefGoogle Scholar
  • Qiang S, Bayati M (2016) Dynamic pricing with demand covariates. Preprint, submitted April 25, https://dx.doi.org/10.2139/ssrn.2765257.Google Scholar
  • Shaw G, Baumann N (2021) 2021 Insurance outlook Accelerating recovery from the pandemic while pivoting to thrive. Deloitte. https://tinyurl.com/2p8dfem8.Google Scholar
  • Singh N (2019) On the journey to data-driven financial services. Forbes (June 20), https://tinyurl.com/3f9yn5w5.Google Scholar
  • SecurityScorecard (2021) 5 key cybersecurity considerations for insurance companies. SecurityScorecard, New York.Google Scholar
  • Shokri R, Stronati M, Song C, Shmatikov V (2017) Membership inference attacks against machine learning models. Proc. IEEE Sympos. on Security and Privacy (IEEE, Piscataway, NJ), 3–18.Google Scholar
  • Smith A, Thakurta A, Upadhyay J (2017) Is interaction necessary for distributed private learning? Proc. IEEE Sympos. on Security and Privacy (IEEE, Piscataway, NJ), 58–77.Google Scholar
  • Stat N (2019) Google is open-sourcing a tool for data scientists to help protect private information. The Verge (September 5), https://tinyurl.com/2wcvfzf5.Google Scholar
  • Stevens L (2017) Retailers turn to silicon valley to lure customers. Wall Street Journal (January 20), https://tinyurl.com/46394c3s.Google Scholar
  • Tsitsiklis JN, Xu K, Xu Z (2018) Private sequential learning. Proc. Conf. On Learning Theory (PMLR, New York), 721–727. Google Scholar
  • Wang D, Xu J (2019) On sparse linear regression in the local differential privacy model. Proc. Internat. Conf. on Machine Learn. (PMLR, New York), 6628–6637.Google Scholar
  • Wang W, Yang L, Chen Y, Zhang Q (2015) A privacy-aware framework for targeted advertising. Comput. Networks 79:17–29.CrossrefGoogle Scholar
  • Wood A, Altman M, Bembenek A, Bun M, Gaboardi M, Honaker J, Nissim K, et al. (2018) Differential privacy: A primer for a non-technical audience. Vanderbilt J. Entertainment Tech. Law 21:209.Google Scholar
  • Xu J, Xu K, Yang D (2021) Optimal query complexity for private sequential learning against eavesdropping. Internat. Conf. Artificial Intelligence and Statistics (PMLR, New York), 2296–2304.Google Scholar
  • Xue Z, Wang Z, Ettl M (2016) Pricing personalized bundles: A new approach and an empirical study. Manufacturing Services Oper. Management 18(1):51–68.LinkGoogle Scholar
  • Yu B (1997) Assouad, fano, and le cam. Festschrift for Lucien Le Cam (Springer, Berlin), 423–435.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.