How Shuffling Works for Effective Time Series Data Protection

Authors

• Matthew J. Schneider, Drexel University
• Jinwook Lee, Drexel University
• Lanqing Du, Marquette UniversityFollow

Document Type

Article

Publication Date

2-2026

Publisher

Springer

Source Publication

Annals of Operations Research

Source ISSN

0254-5330

Original Item ID

DOI: 10.1007/s10479-025-06949-2

Abstract

Many existing data protection methods (DPMs) tend to overlook the use cases of forecasting, that can result in noisy forecasts and decreased accuracy. To address this issue, we develop a novel data protection framework for data providers who prioritize data privacy with minimal loss of forecasting accuracy.

Comments

Annals of Operations Research, Vol. 357, Nos. 2/3 (February 2026): 1165-1190. DOI.

Recommended Citation

Schneider, Matthew J.; Lee, Jinwook; and Du, Lanqing, "How Shuffling Works for Effective Time Series Data Protection" (2026). Management Faculty Research and Publications. 426.
https://epublications.marquette.edu/mgmt_fac/426