Privacy-Preserving Data Imputation

Authors:
Geetha Jagannathan;Rebecca N. Wright
Affiliations:
Stevens Institute of Technology, Hoboken, NJ;Stevens Institute of Technology, Hoboken, NJ
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

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Abstract

In this paper, we investigate privacy-preserving data imputation on distributed databases. We present a privacypreserving protocol for filling in missing values using a lazy decision tree imputation algorithm for data that is horizontally partitioned between two parties. The participants of the protocol learn only the imputed values; the computed decision tree is not learned by either party.