Privacy-preserving data imputation

Geetha Jagannathan, Rebecca N. Wright

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

1 Scopus citations


In this paper, we investigate privacy-preserving data imputation on distributed databases. We present a privacy-preserving 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.

Original languageEnglish (US)
Title of host publicationProceedings - ICDM Workshops 2006 - 6th IEEE International Conference on Data Mining - Workshops
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)0769527027, 9780769527024
StatePublished - 2006
Externally publishedYes

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'Privacy-preserving data imputation'. Together they form a unique fingerprint.

Cite this