Private Data Imputation

TL;DR

This paper introduces optimized protocols for private data imputation that preserve privacy and significantly improve accuracy over local imputation, especially for distributed datasets.

Contribution

It presents the first efficient protocols for private data imputation applicable to horizontally and vertically split datasets, reducing computation to private set intersection.

Findings

20% accuracy improvement for vertically split data

5% accuracy improvement for horizontally split data

Up to 32.7 times better imputation quality in worst-case scenarios

Abstract

Data imputation is an important data preparation task where the data analyst replaces missing or erroneous values to increase the expected accuracy of downstream analyses. The accuracy improvement of data imputation extends to private data analyses across distributed databases. However, existing data imputation methods violate the privacy of the data rendering the privacy protection in the downstream analyses obsolete. We conclude that private data analysis requires private data imputation. In this paper, we present the first optimized protocols for private data imputation. We consider the case of horizontally and vertically split data sets. Our optimization aims to reduce most of the computation to private set intersection (or at least oblivious programmable pseudo-random function) protocols which can be very efficiently computed. We show that private data imputation has -- on average across all evaluated datasets -- an accuracy advantage of 20\% in case of vertically split data and 5\% in case of horizontally split data over imputing data locally. In case of the worst data split we observed that imputing using our method resulted in an increase of up to 32.7 times in the quality of imputation over the vertically split data and 3.4 times in case of horizontally split data. Our protocols are very efficient and run in 2.4 seconds in case of vertically split data and 8.4 seconds in case of horizontally split data for 100,000 records evaluated in the 10 Gbps network setting, performing one data imputation.