An Algorithmic Approach to Managing Supply Chain Data Security: The Differentially Private Newsvendor

References

• Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. Halevi S, Kruegel C, Myers A, eds. Proc. 2016 ACM SIGSAC Conf. Comput. Comm. Security (Association for Computing Machinery, New York), 308–318.Google Scholar
• Abowd JM (2018) The U.S. Census Bureau adopts differential privacy. Guo YK, Farooq F, eds. Proc. 24th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 2867.Google Scholar
• Anand KS, Goyal M (2009) Strategic information management under leakage in a supply chain. Management Sci. 55(3):438–452.Link, Google Scholar
• Asi H, Feldman V, Koren T, Talwar K (2021) Private stochastic convex optimization: Optimal rates in L1 geometry. Meila M, Zhang T, eds. Proc. 38th Internat. Conf. Machine Learn. (PMLR, New York), 393–403.Google Scholar
• Ban GY, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.Link, Google Scholar
• Bassily R, Guzmán C, Menart M (2021) Differentially private stochastic optimization: New results in convex and non-convex settings. Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Wortman Vaughan J, eds. Adv. Neural Inform. Processing Systems 34 (NeurIPS 2021) (Curran Associates Inc., Red Hook, NY), 9317–9329.Google Scholar
• Bassily R, Mohri M, Suresh AT (2022) Differentially private learning with margin guarantees. Koyejo S, Mohamed S, Agarwal A, Belgrave D, Cho K, Oh A, eds. Adv. Neural Inform. Processing Systems 35 (NeurIPS 2022) (Curran Associates Inc., Red Hook, NY), 32127–32141.Google Scholar
• Bassily R, Smith A, Thakurta A (2014) Private empirical risk minimization: Efficient algorithms and tight error bounds. Barak B, ed. Proc. 2014 IEEE 55th Annual Sympos. Foundations Comput. Sci. (IEEE Computer Society, Washington, DC), 464–473.Google Scholar
• Bassily R, Feldman V, Talwar K, Guha Thakurta A (2019) Private stochastic convex optimization with optimal rates. Wallach H, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox E, Garnett R, eds. Adv. Neural Inform. Processing Systems 32 (NeurIPS 2019) (Curran Associates Inc., Red Hook, NY), 11282–11291.Google Scholar
• Bousquet O, Elisseeff A (2002) Stability and generalization. J. Machine Learn. Res. 2(March):499–526.Google Scholar
• Cachon GP, Fisher M (2000) Supply chain inventory management and the value of shared information. Management Sci. 46(8):1032–1048.Link, 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
• Chen X, Liu W, Mao X, Yang Z (2020) Distributed high-dimensional regression under a quantile loss function. J. Machine Learn. Res. 21(182):1–43.Google Scholar
• Cheung WC, Simchi-Levi D (2019) Sampling-based approximation schemes for capacitated stochastic inventory control models. Math. Oper. Res. 44(2):668–692.Link, Google Scholar
• Chu LY, Shanthikumar JG, Shen ZJM (2008) Solving operational statistics via a Bayesian analysis. Oper. Res. Lett. 36(1):110–116.Crossref, Google Scholar
• Croson R, Donohue K, Katok E, Sterman J (2014) Order stability in supply chains: Coordination risk and the role of coordination stock. Production Oper. Management 23(2):176–196.Crossref, Google Scholar
• Cui R, Allon G, Bassamboo A, Van Mieghem JA (2015) Information sharing in supply chains: An empirical and theoretical valuation. Management Sci. 61(11):2803–2824.Link, Google Scholar
• de Fondeville R, Shoemate M, Zhang W, Vadhan S (2023) Protecting high-resolution poverty statistics against disclosure using differential privacy, Swiss Federal Statistical Office. United Nations Economic Commission for Europe. Accessed November 27, 2023, https://unece.org/statistics/documents/2023/08/working-documents/protecting-high-resolution-poverty-statistics.Google Scholar
• Ding B, Kulkarni J, Yekhanin S (2017) Collecting telemetry data privately. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Adv. Neural Inform. Processing Systems 30 (NIPS 2017) (Curran Associates Inc., Red Hook, NY), 3574–3583.Google Scholar
• Dong J, Roth A, Su WJ (2022) Gaussian differential privacy. J. Royal Statist. Soc. Ser. B (Statist. Methodology) 84(1):3–37.Crossref, Google Scholar
• Dukes A, Gal-Or E, Geylani T (2017) Bilateral information sharing and pricing incentives in a retail channel. Ha AY, Tang CS, eds. Handbook of Information Exchange in Supply Chain Management (Springer, Cham, Switzerland), 343–367.Crossref, Google Scholar
• Dwork C, Roth A (2014) The algorithmic foundations of differential privacy. Foundations Trends Theoret. Comput. Sci. 9(3–4):211–407.Crossref, Google Scholar
• Dwork C, McSherry F, Nissim K, Smith A (2006a) Calibrating noise to sensitivity in private data analysis. Halevi S, Rabin T, eds. Theory Cryptography TCC 2006 (Springer, Berlin), 265–284.Google Scholar
• Dwork C, Kenthapadi K, McSherry F, Mironov I, Naor M (2006b) Our data, ourselves: Privacy via distributed noise generation. Vaudenay S, ed. Adv. Cryptology EUROCRYPT 2006 (Springer, Berlin), 486–503.Google Scholar
• Fallah A, Makhdoumi A, Malekian A, Ozdaglar A (2024) Optimal and differentially private data acquisition: Central and local mechanisms. Oper. Res. 72(3):1105–1123.Link, Google Scholar
• Feldman V, Koren T, Talwar K (2020) Private stochastic convex optimization: Optimal rates in linear time. Makarychev K, Makarychev Y, Tulsiani M, Kamath G, Chuzhoy J, eds. Proc. 52nd Annual ACM SIGACT Sympos. Theory Comput. (Association for Computing Machinery, New York), 439–449.Google Scholar
• Feldman V, Mironov I, Talwar K, Thakurta A (2018) Privacy amplification by iteration. Rabani Y, Thorup M, eds. 2018 IEEE 59th Annual Sympos. Foundations Comput. Sci. (FOCS) (IEEE, New York), 521–532.Google Scholar
• Fernandes M, Guerre E, Horta E (2021) Smoothing quantile regressions. J. Bus. Econom. Statist. 39(1):338–357.Crossref, Google Scholar
• Fu X, Chen N, Gao P, Li Y (2025) Privacy-preserving personalized recommender systems. Manufacturing Service Oper. Management, Articles in Advance August 18, https://doi.org/10.1287/msom.2023.0271.Link, Google Scholar
• Garfinkel SL, Bowen CM (2022) Preserving privacy while sharing data. MIT Sloan Management Rev. Accessed November 27, 2023, https://sloanreview.mit.edu/article/preserving-privacy-while-sharing-data/.Google Scholar
• Greenberg A (2016) Apple’s ‘differential privacy’ is about collecting your data—But not your data. Wired. Accessed November 27, 2023, https://www.wired.com/2016/06/apples-differential-privacy-collecting-data/.Google Scholar
• Han J, Hu M, Shen G (2025) Deep neural newsvendor. Management Sci., ePub ahead of print November 3, https://doi.org/10.1287/mnsc.2023.03157.Link, Google Scholar
• Hao L, Naiman DQ (2007) Quantile Regression, Quantitative Applications in the Social Sciences, no. 149 (Sage Publications, Thousand Oaks, CA).Crossref, Google Scholar
• Hartmann F, Kairouz P (2023) Distributed differential privacy for federated learning. Google Research blog. Accessed November 27, 2023, https://ai.googleblog.com/2023/03/distributed-differential-privacy-for.html.Google Scholar
• He X, Pan X, Tan KM, Zhou WX (2023) Smoothed quantile regression with large-scale inference. J. Econom. 232(2):367–388.Crossref, Google Scholar
• Hirschman II, Widder DV (2012) The Convolution Transform (Dover Publications, Mineola, NY).Google Scholar
• Huber J, Müller S, Fleischmann M, Stuckenschmidt H (2019) A data-driven newsvendor problem: From data to decision. Eur. J. Oper. Res. 278(3):904–915.Crossref, Google Scholar
• IBM (2023) Cost of a data breach report 2023. Accessed November 27, 2023, https://www.ibm.com/reports/data-breach.Google Scholar
• Kifer D, Smith A, Thakurta A (2012) Private convex empirical risk minimization and high-dimensional regression. Mannor S, Srebro N, Williamson RC, eds. Proc. 25th Annual Conf. Learn. Theory (PMLR, New York), 25.1–25.40.Google Scholar
• Kilgore A (2020) Data privacy vs. data security: Overcoming our fear of sharing data. ORMS Today (April 23), https://pubsonline.informs.org/do/10.1287/orms.2020.02.37p/full/.Google Scholar
• Kong G, Rajagopalan S, Zhang H (2013) Revenue sharing and information leakage in a supply chain. Management Sci. 59(3):556–572.Link, Google Scholar
• Kong G, Rajagopalan S, Zhang H (2017) Information leakage in supply chains. Ha AY, Tang CS, eds. Handbook of Information Exchange in Supply Chain Management (Springer, Cham, Switzerland), 313–341.Crossref, Google Scholar
• Kulkarni J, Lee YT, Liu D (2021) Private non-smooth ERM and SCO in subquadratic steps. Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Wortman Vaughan J, eds. Adv. Neural Inform. Processing Systems 34 (NeurIPS 2021) (Curran Associates Inc., Red Hook, NY), 4053–4064.Google Scholar
• Kumar S, Mallipeddi RR (2022) Impact of cybersecurity on operations and supply chain management: Emerging trends and future research directions. Production Oper. Management 31(12):4488–4500.Crossref, Google Scholar
• Kumar S, Tang CS (2022) Expanding the boundaries of the discipline: The 30th-anniversary issue of Production and Operations Management. Production Oper. Management 31(12):4257–4261.Crossref, Google Scholar
• Lariviere MA, Porteus EL (2001) Selling to the newsvendor: An analysis of price-only contracts. Manufacturing Service Oper. Management 3(4):293–305.Link, Google Scholar
• Lee HL, Whang S (2000) Information sharing in a supply chain. Internat. J. Manufacturing Tech. Management 1(1):79–93.Crossref, Google Scholar
• Lei Y, Miao S, Momot R (2024) Privacy-preserving personalized revenue management. Management Sci. 70(7):4875–4892.Link, Google Scholar
• Levi R, Perakis G, Uichanco J (2015) The data-driven newsvendor problem: New bounds and insights. Oper. Res. 63(6):1294–1306.Link, Google Scholar
• Levi R, Roundy RO, Shmoys DB (2007) Provably near-optimal sampling-based policies for stochastic inventory control models. Math. Oper. Res. 32(4):821–839.Link, Google Scholar
• Li L (2002) Information sharing in a supply chain with horizontal competition. Management Sci. 48(9):1196–1212.Link, Google Scholar
• Lin M, Huh WT, Krishnan H, Uichanco J (2022) Data-driven newsvendor problem: Performance of the sample average approximation. Oper. Res. 70(4):1996–2012.Link, Google Scholar
• Liyanage LH, Shanthikumar JG (2005) A practical inventory control policy using operational statistics. Oper. Res. Lett. 33(4):341–348.Crossref, Google Scholar
• Massimino B, Gray JV, Lan Y (2018) On the inattention to digital confidentiality in operations and supply chain research. Production Oper. Management 27(8):1492–1515.Crossref, Google Scholar
• Oroojlooyjadid A, Snyder LV, Takáč M (2020) Applying deep learning to the newsvendor problem. IISE Trans. 52(4):444–463.Crossref, Google Scholar
• Özer Ö, Zheng Y, Chen KY (2011) Trust in forecast information sharing. Management Sci. 57(6):1111–1137.Link, Google Scholar
• Parikh N, Boyd S (2014) Proximal algorithms. Foundations Trends Optim. 1(3):127–239.Crossref, Google Scholar
• Qi M, Cao Y, Shen ZJ (2022) Distributionally robust conditional quantile prediction with fixed design. Management Sci. 68(3):1639–1658.Link, Google Scholar
• Qi M, Shi Y, Qi Y, Ma C, Yuan R, Wu D, Shen ZJ (2023) A practical end-to-end inventory management model with deep learning. Management Sci. 69(2):759–773.Link, Google Scholar
• Ramamurthy V, Shanthikumar JG, Shen ZJM (2012) Inventory policy with parametric demand: Operational statistics, linear correction, and regression. Production Oper. Management 21(2):291–308.Crossref, Google Scholar
• RiskLens (2023) 2023 cybersecurity risk report. Accessed November 27, 2023, https://www.risklens.com/2023-cybersecurity-risk-report.Google Scholar
• Selvi A, Liu H, Wiesemann W (2025) Differential privacy via distributionally robust optimization. Oper. Res., ePub ahead of print March 10, https://doi.org/10.1287/opre.2023.0218.Link, Google Scholar
• Wang YX, Lei J, Fienberg SE (2016) Learning with differential privacy: Stability, learnability and the sufficiency and necessity of ERM principle. J. Machine Learn. Res. 17(183):1–40.Google Scholar
• Zhao T, Zhou WX, Wang L (2024) Private optimal inventory policy learning for feature-based newsvendor with unknown demand. Management Sci. 71(7):6092–6111.Link, Google Scholar