Quantum Privacy-Preserving Data Mining

TL;DR

This paper introduces a quantum protocol for privacy-preserving data mining of association rules in vertically partitioned databases, enhancing privacy and efficiency over classical methods.

Contribution

It presents a novel quantum protocol that improves privacy preservation and reduces computational and communication costs in data mining.

Findings

Quantum protocol enhances privacy over classical methods

Reduces computational complexity exponentially

Decreases communication costs significantly

Abstract

Data mining is a key technology in big data analytics and it can discover understandable knowledge (patterns) hidden in large data sets. Association rule is one of the most useful knowledge patterns, and a large number of algorithms have been developed in the data mining literature to generate association rules corresponding to different problems and situations. Privacy becomes a vital issue when data mining is used to sensitive data sets like medical records, commercial data sets and national security. In this Letter, we present a quantum protocol for mining association rules on vertically partitioned databases. The quantum protocol can improve the privacy level preserved by known classical protocols and at the same time it can exponentially reduce the computational complexity and communication cost.