Privacy-Preserving Personalized Recommender Systems

References

• Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. Proc. ACM SIGSAC Conf. Comput. Comm. Security (Association for Computing Machinery, New York), 308–318.Google Scholar
• Abowd JM (2018) The US Census Bureau adopts differential privacy. Proc. 24th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining, 2867–2867.Google Scholar
• Acemoglu D, Makhdoumi A, Malekian A, Ozdaglar A (2017) Privacy-constrained network formation. Games Econom. Behav. 105:255–275.Crossref, Google Scholar
• Acquisti A, Brandimarte L, Loewenstein G (2020) Secrets and likes: The drive for privacy and the difficulty of achieving it in the digital age. J. Consumer Psych. 30(4):736–758.Crossref, Google Scholar
• Acquisti A, Taylor C, Wagman L (2016) The economics of privacy. J. Econom. Literature 54(2):442–492.Crossref, Google Scholar
• Anderson ET, Simester DI (2010) Price stickiness and customer antagonism. Quart. J. Econom. 125(2):729–765.Crossref, Google Scholar
• Aouad A, Farias V, Levi R, Segev D (2018) The approximability of assortment optimization under ranking preferences. Oper. Res. 66(6):1661–1669.Link, Google Scholar
• Armstrong M (2006) Competition in two-sided markets. RAND J. Econom. 37(3):668–691.Crossref, Google Scholar
• Bonatti A, Huang Y, Villas-Boas JM (2023) A theory of the effects of privacy. CEPR Discussion Paper No. 18459, CEPR, Paris. Google Scholar
• Casadesus-Masanell R, Hervas-Drane A (2015) Competing with privacy. Management Sci. 61(1):229–246.Link, Google Scholar
• Chaudhuri K, Monteleoni C (2008) Privacy-preserving logistic regression. Koller D, Schuurmans D, Bengio Y, Bottou L, eds. Advances in Neural Information Processing Systems, vol. 21 (Curran Associates, Inc., Red Hook, NY).Google Scholar
• Chen N, Li A, Yang S (2021) Revenue maximization and learning in products ranking. Proc. 22nd ACM Conf. Econom. Comput., 316–317.Google Scholar
• Chen X, Miao S, Wang Y (2023) Differential privacy in personalized pricing with nonparametric demand models. Oper. Res. 71(2):581–602.Link, Google Scholar
• Chen X, Simchi-Levi D, Wang Y (2022) Privacy-preserving dynamic personalized pricing with demand learning. Management Sci. 68(7):4878–4898.Link, Google Scholar
• Cohen MC (2018) Big data and service operations. Production Oper. Management 27(9):1709–1723.Crossref, Google Scholar
• Cohen MC, Elmachtoub AN, Lei X (2022) Price discrimination with fairness constraints. Management Sci. 68(12):8536–8552.Link, Google Scholar
• Derakhshan M, Golrezaei N, Manshadi V, Mirrokni V (2022) Product ranking on online platforms. Management Sci. 68(6):3975–4753.Google Scholar
• Dwork C, Roth A (2014) The algorithmic foundations of differential privacy. Foundations Trends Theoretical Comput. Sci. 9(3–4):211–407.Crossref, Google Scholar
• Eilat R, Eliaz K, Mu X (2021) Bayesian privacy. Theoretical Econom. 16(4):1557–1603.Crossref, Google Scholar
• Fainmesser IP, Galeotti A, Momot R (2023) Digital privacy. Management Sci. 69(6):3157–3173.Link, Google Scholar
• Fallah A, Makhdoumi A, Malekian A, Ozdaglar A (2022) Optimal and differentially private data acquisition: Central and local mechanisms. Proc. 23rd ACM Conf. Econom. Comput. (ACM, New York), 1141–1141.Google Scholar
• Farias VF, Jagabathula S, Shah D (2013) A nonparametric approach to modeling choice with limited data. Management Sci. 59(2):305–322.Link, Google Scholar
• Ferreira KJ, Parthasarathy S, Sekar S (2022) Learning to rank an assortment of products. Management Sci. 68(3):1828–1848.Link, Google Scholar
• Fioretto F, Tran C, Van Hentenryck P, Zhu K (2022) Differential privacy and fairness in decisions and learning tasks: A survey. Preprint, submitted February 16, https://arxiv.org/abs/2202.08187.Google Scholar
• Fu R, Aseri M, Singh PV, Srinivasan K (2022) “Un”fair machine learning algorithms. Management Sci. 68(6):4173–4195.Link, Google Scholar
• Gallego G, Van Ryzin G (1994) Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Sci. 40(8):999–1020.Link, Google Scholar
• Gallego G, Li A, Truong V-A, Wang X (2020) Approximation algorithms for product framing and pricing. Oper. Res. 68(1):134–160.Link, Google Scholar
• Gao P, Ma Y, Chen N, Gallego G, Li A, Rusmevichientong P, Topaloglu H (2021) Assortment optimization and pricing under the multinomial logit model with impatient customers: Sequential recommendation and selection. Oper. Res. 69(5):1509–1532.Link, Google Scholar
• Goldfarb A, Tucker CE (2011) Privacy regulation and online advertising. Management Sci. 57(1):57–71.Link, Google Scholar
• Goldfarb A, Jin G, Sudhir K (2020) Introduction to the special issue on consumer protection. Marketing Sci. 39(1):1–4.Link, Google Scholar
• He K, Mu X (2014) Differentially private and incentive compatible recommendation system for the adoption of network goods. Proc. 15th ACM Conf. Econom. Comput. (ACM, New York), 949–966.Google Scholar
• Honhon D, Jonnalagedda S, Pan XA (2012) Optimal algorithms for assortment selection under ranking-based consumer choice models. Manufacturing Service Oper. Management 14(2):279–289.Link, Google Scholar
• Hu M, Momot R, Wang J (2022) Privacy management in service systems. Manufacturing Service Oper. Management 24(5):2761–2779.Link, Google Scholar
• Hunt D (2021) Can algorithms be ethical? Accessed February 24, 2025, https://pubsonline.informs.org/do/10.1287/orms.2021.06.12/full/.Google Scholar
• Ichihashi S (2020) Online privacy and information disclosure by consumers. Amer. Econom. Rev. 110(2):569–595.Crossref, Google Scholar
• Iyer G, Soberman D, Villas-Boas JM (2005) The targeting of advertising. Marketing Sci. 24(3):461–476.Link, Google Scholar
• Johnson GA, Shriver SK, Du S (2020) Consumer privacy choice in online advertising: Who opts out and at what cost to industry? Marketing Sci. 39(1):33–51.Link, Google Scholar
• Karmarkar N (1984) A new polynomial-time algorithm for linear programming. Combinatorica 4(4):373–395.Crossref, Google Scholar
• Ke TT, Sudhir K (2023) Privacy rights and data security: GDPR and personal data markets. Management Sci. 69(8):4389–4412.Link, Google Scholar
• Laussel D, Van Long N, Resende J (2023) Profit effects of consumers’ identity management: A dynamic model. Management Sci. 69(6):3602–3615.Link, Google Scholar
• Lei Y, Miao S, Momot R (2024) Privacy-preserving personalized revenue management. Management Sci. 70(7):4875–4892.Link, Google Scholar
• Li X, Li KJ (2023) Beating the algorithm: Consumer manipulation, personalized pricing, and big data management. Manufacturing Service Oper. Management 25(1):36–49.Link, Google Scholar
• Liu Z, Zhang DJ, Zhang F (2021) Information sharing on retail platforms. Manufacturing Service Oper. Management 23(3):606–619.Link, Google Scholar
• Lobel I (2021) Revenue management and the rise of the algorithmic economy. Management Sci. 67(9):5389–5398.Link, Google Scholar
• Makhdoumi A, Fawaz N (2013) Privacy-utility tradeoff under statistical uncertainty. Proc. 51st Ann. Allerton Conf. Comm. Control Comput. (Allerton) (IEEE, Piscataway, NJ), 1627–1634.Google Scholar
• Margulis ST (2003) Privacy as a social issue and behavioral concept. J. Soc. Issues 59(2):243–261.Crossref, Google Scholar
• Montes R, Sand-Zantman W, Valletti T (2019) The value of personal information in online markets with endogenous privacy. Management Sci. 65(3):1342–1362.Link, Google Scholar
• Mosteller F (1946) On some useful inefficient statistics. Ann. Math. Statist. 17(4):377–408.Crossref, Google Scholar
• Negahban S, Oh S, Shah D (2017) Rank centrality: Ranking from pairwise comparisons. Oper. Res. 65(1):266–287.Link, Google Scholar
• Posner RA (1981) The economics of privacy. Amer. Econom. Rev. 71(2):405–409.Google Scholar
• Sturt B (2025) The value of robust assortment optimization under ranking-based choice models. Management Sci. 71(5):4246–4265.Link, Google Scholar
• Tsitsiklis JN, Xu K, Xu Z (2021) Private sequential learning. Oper. Res. 69(5):1575–1590.Link, Google Scholar
• Valletti T, Wu J (2020) Consumer profiling with data requirements: Structure and policy implications. Production Oper. Management 29(2):309–329.Crossref, Google Scholar
• Wang R (2022) What is the impact of nonrandomness on random choice models? Manufacturing Service Oper. Management 24(1):485–503.Link, Google Scholar
• Wang Q, Huang Y, Jasin S, Singh PV (2022) Algorithmic transparency with strategic users. Management Sci. 69(4):2297–2317.Link, Google Scholar
• Wauthier F, Jordan M, Jojic N (2013) Efficient ranking from pairwise comparisons. Proc. Internat. Conf. Machine Learn. (PMLR), 109–117.Google Scholar
• Xu F, Wang X, Zhang F (2025) Consumer privacy in online retail supply chains. Management Sci. Forthcoming.Link, Google Scholar
• Xu J, Xu K, Yang D (2021) Learner-private convex optimization. Proc. Internat. Conf. Machine Learn. (PMLR, New York), 11570–11580.Google Scholar
• Yang Q, Liu Y, Cheng Y, Kang Y, Chen T, Yu H (2019) Federated learning. Synthesis Lectures Artificial Intelligence Machine Learn. 13(3):1–207.Crossref, Google Scholar
• Zhang J, Moon K, Veeraraghavan SK (2022) Does fake news create echo chambers? Preprint, submitted June 29, https://doi.org/10.2139/ssrn.4144897.Google Scholar
• Zhang M, Wei E, Berry R, Huang J (2023) Age-dependent differential privacy. IEEE Trans. Inform. Theory 70(2):1300–1319.Crossref, Google Scholar