TY - GEN
T1 - Accurate estimation of structural equation models with remote partitioned data
AU - Snoke, Joshua
AU - Brick, Timothy
AU - Slavković, Aleksandra
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - This paper focuses on a privacy paradigm centered around providing access to researchers to remotely carry out analyses on sensitive data stored behind firewalls. We develop and demonstrate a method for accurate estimation of structural equation models (SEMs) for arbitrarily partitioned data. We show that under a certain set of assumptions our method for estimation across these partitions achieves identical results as estimation with the full data. We consider two situations: (i) a standard setting with a trusted central server and (ii) a round-robin setting in which none of the parties are fully trusted, and extend them in two specific ways. First, we formulate our methods specifically for SEMs, which have become increasingly common models in psychology, human development, and the behavioral sciences. Secondly, our methods work for horizontal, vertical, and complex partitions without needing different routines. In application, this method will serve to increase opportunities for research by allowing SEM estimation without transfer or combination of data. We demonstrate our methods with both simulated and real data examples.
AB - This paper focuses on a privacy paradigm centered around providing access to researchers to remotely carry out analyses on sensitive data stored behind firewalls. We develop and demonstrate a method for accurate estimation of structural equation models (SEMs) for arbitrarily partitioned data. We show that under a certain set of assumptions our method for estimation across these partitions achieves identical results as estimation with the full data. We consider two situations: (i) a standard setting with a trusted central server and (ii) a round-robin setting in which none of the parties are fully trusted, and extend them in two specific ways. First, we formulate our methods specifically for SEMs, which have become increasingly common models in psychology, human development, and the behavioral sciences. Secondly, our methods work for horizontal, vertical, and complex partitions without needing different routines. In application, this method will serve to increase opportunities for research by allowing SEM estimation without transfer or combination of data. We demonstrate our methods with both simulated and real data examples.
UR - http://www.scopus.com/inward/record.url?scp=84987941883&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84987941883&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-45381-1_15
DO - 10.1007/978-3-319-45381-1_15
M3 - Conference contribution
AN - SCOPUS:84987941883
SN - 9783319453804
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 190
EP - 209
BT - Privacy in Statistical Databases - UNESCO Chair in Data Privacy International Conference, PSD 2016, Proceedings
A2 - Domingo-Ferrer, Josep
A2 - Pejić-Bach, Mirjana
PB - Springer Verlag
T2 - International Conference on Privacy in Statistical Databases, PSD 2016
Y2 - 14 September 2016 through 16 September 2016
ER -