NCRN Newsletter: Volume 2 - Issue 4 Vilhuber, Lars; Karr, Alan; Reiter, Jerome; Abowd, John; Nunnelly, Jamie Overview of activities at NSF-Census Research Network nodes from September 2015 through December 2015. NCRN Newsletter Vol. 2, Issue 4: January 28, 2016.
PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/42394 ER - TY - RPRT T1 - NCRN Newsletter: Volume 3 - Issue 1 Y1 - 2016 A1 - Vilhuber, Lars A1 - Karr, Alan A1 - Reiter, Jerome A1 - Abowd, John A1 - Nunnelly, Jamie AB - NCRN Newsletter: Volume 3 - Issue 1 Vilhuber, Lars; Karr, Alan; Reiter, Jerome; Abowd, John; Nunnelly, Jamie Overview of activities at NSF-Census Research Network nodes from January 2016 through May 2016. NCRN Newsletter Vol. 3, Issue 1: June 10, 2016 PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/44199 ER - TY - RPRT T1 - NCRN Newsletter: Volume 2 - Issue 1 Y1 - 2015 A1 - Vilhuber, Lars A1 - Karr, Alan A1 - Reiter, Jerome A1 - Abowd, John A1 - Nunnelly, Jamie AB - NCRN Newsletter: Volume 2 - Issue 1 Vilhuber, Lars; Karr, Alan; Reiter, Jerome; Abowd, John; Nunnelly, Jamie Overview of activities at NSF-Census Research Network nodes from October 2014 to January 2015. NCRN Newsletter Vol. 2, Issue 1: January 30, 2015. PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/40193 ER - TY - RPRT T1 - NCRN Newsletter: Volume 2 - Issue 2 Y1 - 2015 A1 - Vilhuber, Lars A1 - Karr, Alan A1 - Reiter, Jerome A1 - Abowd, John A1 - Nunnelly, Jamie AB - NCRN Newsletter: Volume 2 - Issue 2 Vilhuber, Lars; Karr, Alan; Reiter, Jerome; Abowd, John; Nunnelly, Jamie Overview of activities at NSF-Census Research Network nodes from January 2015 to May 2015. NCRN Newsletter Vol. 2, Issue 2: May 12, 2015. PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/40194 ER - TY - RPRT T1 - NCRN Newsletter: Volume 2 - Issue 2 Y1 - 2015 A1 - Vilhuber, Lars A1 - Karr, Alan A1 - Reiter, Jerome A1 - Abowd, John A1 - Nunnelly, Jamie AB - NCRN Newsletter: Volume 2 - Issue 2 Vilhuber, Lars; Karr, Alan; Reiter, Jerome; Abowd, John; Nunnelly, Jamie Overview of activities at NSF-Census Research Network nodes from February 2015 to May 2015. NCRN Newsletter Vol. 2, Issue 2: May 12, 2015. PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/44200 ER - TY - RPRT T1 - NCRN Newsletter: Volume 2 - Issue 3 Y1 - 2015 A1 - Vilhuber, Lars A1 - Karr, Alan A1 - Reiter, Jerome A1 - Abowd, John A1 - Nunnelly, Jamie AB -NCRN Newsletter: Volume 2 - Issue 3 Vilhuber, Lars; Karr, Alan; Reiter, Jerome; Abowd, John; Nunnelly, Jamie Overview of activities at NSF-Census Research Network nodes from June 2015 through August 2015. NCRN Newsletter Vol. 2, Issue 3: September 15, 2015.
PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/42393 ER - TY - RPRT T1 - NCRN Newsletter: Volume 1 - Issue 2 Y1 - 2014 A1 - Vilhuber, Lars A1 - Karr, Alan A1 - Reiter, Jerome A1 - Abowd, John A1 - Nunnelly, Jamie AB - NCRN Newsletter: Volume 1 - Issue 2 Vilhuber, Lars; Karr, Alan; Reiter, Jerome; Abowd, John; Nunnelly, Jamie Overview of activities at NSF-Census Research Network nodes from November 2013 to March 2014. NCRN Newsletter Vol. 1, Issue 2: March 20, 2014 PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/40233 ER - TY - RPRT T1 - NCRN Newsletter: Volume 1 - Issue 3 Y1 - 2014 A1 - Vilhuber, Lars A1 - Karr, Alan A1 - Reiter, Jerome A1 - Abowd, John A1 - Nunnelly, Jamie AB - NCRN Newsletter: Volume 1 - Issue 3 Vilhuber, Lars; Karr, Alan; Reiter, Jerome; Abowd, John; Nunnelly, Jamie Overview of activities at NSF-Census Research Network nodes from March 2014 to July 2014. NCRN Newsletter Vol. 1, Issue 3: July 23, 2014 PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/40234 ER - TY - RPRT T1 - NCRN Newsletter: Volume 1 - Issue 4 Y1 - 2014 A1 - Vilhuber, Lars A1 - Karr, Alan A1 - Reiter, Jerome A1 - Abowd, John A1 - Nunnelly, Jamie AB - NCRN Newsletter: Volume 1 - Issue 4 Vilhuber, Lars; Karr, Alan; Reiter, Jerome; Abowd, John; Nunnelly, Jamie Overview of activities at NSF-Census Research Network nodes from July 2014 to October 2014. NCRN Newsletter Vol. 1, Issue 4: October 15, 2014 PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/40192 ER - TY - RPRT T1 - NCRN Newsletter: Volume 1 - Issue 1 Y1 - 2013 A1 - Vilhuber, Lars A1 - Karr, Alan A1 - Reiter, Jerome A1 - Abowd, John A1 - Nunnelly, Jamie AB - NCRN Newsletter: Volume 1 - Issue 1 Vilhuber, Lars; Karr, Alan; Reiter, Jerome; Abowd, John; Nunnelly, Jamie Overview of activities at NSF-Census Research Network nodes from July 2013 to November 2013. NCRN Newsletter Vol. 1, Issue 1: November 17, 2013 PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/40232 ER - TY - RPRT T1 - Estimating identification disclosure risk using mixed membership models Y1 - 2011 A1 - Manrique-Vallier, Daniel A1 - Reiter, Jerome AB - Estimating identification disclosure risk using mixed membership models Manrique-Vallier, Daniel; Reiter, Jerome Statistical agencies and other organizations that disseminate data are obligated to protect data subjects' confi dentiality. For example, ill-intentioned individuals might link data subjects to records in other databases by matching on common characteristics (keys). Successful links are particularly problematic for data subjects with combinations of keys that are unique in the population. Hence, as part of their assessments of disclosure risks, many data stewards estimate the probabilities that sample uniques on sets of discrete keys are also population uniques on those keys. This is typically done using log-linear modeling on the keys. However, log-linear models can yield biased estimates of cell probabilities for sparse contingency tables with many zero counts, which often occurs in databases with many keys. This bias can result in unreliable estimates of probabilities of uniqueness and, hence, misrepresentations of disclosure risks. We propose an alternative to log-linear models for datasets with sparse keys based on a Bayesian version of grade of membership (GoM) models. We present a Bayesian GoM model for multinomial variables and off er an MCMC algorithm for fitting the model. We evaluate the approach by treating data from a recent US Census Bureau public use microdata sample as a population, taking simple random samples from that population, and benchmarking estimated probabilities of uniqueness against population values. Compared to log-linear models, GoM models provide more accurate estimates of the total number of uniques in the samples. Additionally, they offer record-level predictions of uniqueness that dominate those based on log-linear models. PB - Duke University / National Institute of Statistical Sciences (NISS) UR - http://hdl.handle.net/1813/33184 ER -