Accurate estimation of structural equation models with remote partitioned data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publicationPrivacy in Statistical Databases - UNESCO Chair in Data Privacy International Conference, PSD 2016, Proceedings
EditorsJosep Domingo-Ferrer, Mirjana Pejić-Bach
PublisherSpringer Verlag
Pages190-209
Number of pages20
ISBN (Print)9783319453804
DOIs
StatePublished - Jan 1 2016
EventInternational Conference on Privacy in Statistical Databases, PSD 2016 - Dubrovnik, Croatia
Duration: Sep 14 2016Sep 16 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9867 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Conference on Privacy in Statistical Databases, PSD 2016
CountryCroatia
CityDubrovnik
Period9/14/169/16/16

Fingerprint

Structural Equation Model
Partition
Firewall
Servers
Demonstrate
Privacy
Horizontal
Server
Vertical
Paradigm

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Snoke, J., Brick, T. R., & Slavkovic, A. B. (2016). Accurate estimation of structural equation models with remote partitioned data. In J. Domingo-Ferrer, & M. Pejić-Bach (Eds.), Privacy in Statistical Databases - UNESCO Chair in Data Privacy International Conference, PSD 2016, Proceedings (pp. 190-209). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9867 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-45381-1_15
Snoke, Joshua ; Brick, Timothy Raymond ; Slavkovic, Aleksandra B. / Accurate estimation of structural equation models with remote partitioned data. Privacy in Statistical Databases - UNESCO Chair in Data Privacy International Conference, PSD 2016, Proceedings. editor / Josep Domingo-Ferrer ; Mirjana Pejić-Bach. Springer Verlag, 2016. pp. 190-209 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Snoke, J, Brick, TR & Slavkovic, AB 2016, Accurate estimation of structural equation models with remote partitioned data. in J Domingo-Ferrer & M Pejić-Bach (eds), Privacy in Statistical Databases - UNESCO Chair in Data Privacy International Conference, PSD 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9867 LNCS, Springer Verlag, pp. 190-209, International Conference on Privacy in Statistical Databases, PSD 2016, Dubrovnik, Croatia, 9/14/16. https://doi.org/10.1007/978-3-319-45381-1_15

Accurate estimation of structural equation models with remote partitioned data. / Snoke, Joshua; Brick, Timothy Raymond; Slavkovic, Aleksandra B.

Privacy in Statistical Databases - UNESCO Chair in Data Privacy International Conference, PSD 2016, Proceedings. ed. / Josep Domingo-Ferrer; Mirjana Pejić-Bach. Springer Verlag, 2016. p. 190-209 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9867 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.

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SN - 9783319453804

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Snoke J, Brick TR, Slavkovic AB. Accurate estimation of structural equation models with remote partitioned data. In Domingo-Ferrer J, Pejić-Bach M, editors, Privacy in Statistical Databases - UNESCO Chair in Data Privacy International Conference, PSD 2016, Proceedings. Springer Verlag. 2016. p. 190-209. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-45381-1_15