TY - GEN
T1 - An application of differentially private linear mixed modeling
AU - Abowd, John M.
AU - Schneider, Matthew J.
PY - 2011
Y1 - 2011
N2 - We consider a differentially private MLE for the linear mixed-effects model with normal random errors. This model is important because it is frequently used in small area estimation and detailed industry tabulations that present significant challenges for confidentiality protection of the underlying data. The differentially private estimator performs well compared to the regular MLE, and deteriorates as the protection increases, for a problem in which small-area variation is at the county level. More dimensions of random effects are needed to adequately represent the time-dimension of the data, and for these cases the differentially private MLE cannot be computed.
AB - We consider a differentially private MLE for the linear mixed-effects model with normal random errors. This model is important because it is frequently used in small area estimation and detailed industry tabulations that present significant challenges for confidentiality protection of the underlying data. The differentially private estimator performs well compared to the regular MLE, and deteriorates as the protection increases, for a problem in which small-area variation is at the county level. More dimensions of random effects are needed to adequately represent the time-dimension of the data, and for these cases the differentially private MLE cannot be computed.
KW - Differential privacy
KW - EBLUP
KW - Linear mixed models
KW - MLE
KW - Privacy-preserving datamining
KW - Quarterly workforce indicators
KW - REML
KW - Statistical disclosure limitation
UR - http://www.scopus.com/inward/record.url?scp=84857181966&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84857181966&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2011.26
DO - 10.1109/ICDMW.2011.26
M3 - Conference contribution
AN - SCOPUS:84857181966
SN - 9780769544090
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 614
EP - 619
BT - Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
T2 - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Y2 - 11 December 2011 through 11 December 2011
ER -