Privacy-preserving imputation of missing data

Authors:
Geetha Jagannathan;Rebecca N. Wright
Affiliations:
Stevens Institute of Technology, Hoboken, NJ 07030, USA;Stevens Institute of Technology, Hoboken, NJ 07030, USA
Venue:
Data & Knowledge Engineering
Year:
2008

Citing 13
Cited 1

Statistical analysis with missing data

Statistical analysis with missing data
C4.5: programs for machine learning

C4.5: programs for machine learning
Oblivious transfer and polynomial evaluation

STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Privacy-preserving data mining

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient oblivious transfer protocols

SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Communication preserving protocols for secure function evaluation

STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Imputation of Missing Data in Industrial Databases

Applied Intelligence
The CN2 Induction Algorithm

Machine Learning
Induction of Decision Trees

Machine Learning
Foundations of Cryptography: Volume 1

Foundations of Cryptography: Volume 1
Privacy-Preserving Data Imputation

ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Towards Automatic Auditing of Records

IEEE Transactions on Software Engineering
Lazy decision trees

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1

An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks

Data & Knowledge Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

When releasing microdata for research purposes, one needs to preserve the privacy of respondents while maximizing data utility. An approach that has been studied extensively in recent years is to use anonymization techniques such as generalization and ...