Privacy-preserving imputation of missing data

Abstract

Handling missing data is a critical step to ensuring good results in data mining. Like most data mining algorithms, existing privacy-preserving data mining algorithms assume data is complete. In order to maintain privacy in the data mining process while cleaning data, privacy-preserving methods of data cleaning are required. In this paper, we address the problem of privacy-preserving data imputation of missing data. 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.