A privacy preserving Jaccard similarity function for mining encrypted data

Abstract

Due to advances in data collection and increasing dependency on data mining experts, preserving privacy of the data is a major concern when mining the data. Most of the classifier implementations for data mining have the tradeoff between classification accuracy and maintenance of data privacy. Another important aspect in distance-based classifiers is to accurately compute distance (or similarity) between two or more data points. In privacy preserving data mining techniques, providing a suitable distance measure to classify the data while maintaining data privacy is a challenging task. In this paper, we present an approach to compute similarity between two encrypted data points. We augmented Jaccard similarity function with Private Equality Test protocol facilitating a semi honest third party to conduct the equality test. The proposed privacy preserving scheme provides an efficient mechanism for similarity computation with reduced communication cost for mining the data.