An Application of Differentially Private Linear Mixed Modeling. - John M. Abowd; Matthew J. Schneider (ICDM Workshops, 2011)

Differential Privacy Applications to Bayesian and Linear Mixed Model Estimation. - John M. Abowd; Matthew J. Schneider; Lars Vilhuber (J. Priv. Confidentiality, 2013)

A Flexible Method for Protecting Marketing Data: An Application to Point-of-Sale Data. - Matthew J. Schneider; Sharan Jagpal; Sachin Gupta; Shaobo Li; Yan Yu (Mark. Sci., 2018)

Time series data identification and optimization with the measurements of potential bias and discrimination issues based on pseudo-Boolean functions. - Jinwook Lee; Lanqing Du; Jade Zhang; Matthew J. Schneider (ISAIM, 2022)

Reidentification Risk in Panel Data: Protecting for k-Anonymity. - Shaobo Li; Matthew J. Schneider; Yan Yu; Sachin Gupta (Inf. Syst. Res., 2023)

Protecting the anonymity of online users through Bayesian data synthesis. - Matthew J. Schneider; Jingchen Hu; Shawn Mankad; Cameron D. Bale (Expert Syst. Appl., 2023)

Geometric series representation for robust bounds of exponential smoothing difference between protected and confidential data. - Jinwook Lee; Matthew J. Schneider (Ann. Oper. Res., 2024)

Legally Anonymizing Location Data Under the GDPR. - Cameron D. Bale; Jordan L. Fischer; Matthew J. Schneider; Steven Weber 001; Suzanne Chang (Trans. Data Priv., 2024)