Abstract
The machine learning community has focused on confidentiality problems associated with statistical analyses that “integrate” data stored in multiple, distributed databases where there are barriers to simply integrating the databases. This paper discusses various techniques which can be used to perform statistical analysis for categorical data, especially in the form of log-linear analysis and logistic regression over partitioned databases, while limiting confidentiality concerns. We show how ideas from the current literature that focus on “secure” summations and secure regression analysis can be adapted or generalized to the categorical data setting.
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Fienberg, S.E., Fulp, W.J., Slavkovic, A.B., Wrobel, T.A. (2006). “Secure” Log-Linear and Logistic Regression Analysis of Distributed Databases. In: Domingo-Ferrer, J., Franconi, L. (eds) Privacy in Statistical Databases. PSD 2006. Lecture Notes in Computer Science, vol 4302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11930242_24
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DOI: https://doi.org/10.1007/11930242_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49330-3
Online ISBN: 978-3-540-49332-7
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