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Even when raw demand data are never shared, observers may reverse-engineer the data from small changes in order quantities. This risk is particularly acute in supply chains, where decisions are frequent, data-intensive, and easily observable. To mitigate this threat, the paper develops a suite of differentially private algorithms for the contextual newsvendor problem. These methods introduce carefully calibrated randomness into the newsvendor model, ensuring strong privacy protection under differential privacy while maintaining near-optimal operational performance. The authors also identify key drivers of the cost of privacy\u2014data set size, contextual richness, and product variety\u2014offering actionable guidance for firms seeking secure yet efficient data-driven operations. Finally, they demonstrate that privacy-preserving decisions can distort demand signals and reduce upstream supplier profits, highlighting important supply-chain-wide implications of data protection.<\/jats:p>","DOI":"10.1287\/opre.2024.1213","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T15:47:49Z","timestamp":1766591269000},"page":"958-983","source":"Crossref","is-referenced-by-count":2,"title":["An Algorithmic Approach to Managing Supply Chain Data Security: The Differentially Private Newsvendor"],"prefix":"10.1287","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0627-0901","authenticated-orcid":false,"given":"Du","family":"Chen","sequence":"first","affiliation":[{"name":"Nanyang Business School, Nanyang Technological University, 639798 Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4278-5961","authenticated-orcid":false,"given":"Geoffrey A.","family":"Chua","sequence":"additional","affiliation":[{"name":"Nanyang Business School, Nanyang Technological University, 639798 Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"109","reference":[{"key":"B1","doi-asserted-by":"crossref","unstructured":"Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. 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