Semi-Trusted Mixer Based Privacy Preserving Distributed Data Mining for Resource Constrained Devices

TL;DR

This paper introduces a low-computation homomorphic privacy-preserving association rule mining algorithm suitable for resource-constrained devices, utilizing a semi-trusted mixer to securely aggregate encrypted counts.

Contribution

It presents a novel, efficient privacy-preserving data mining method using a semi-trusted mixer, reducing computational complexity for resource-limited devices.

Findings

The proposed algorithm is secure and effective based on security proofs.

Performance analysis shows it is computationally efficient for resource-constrained devices.

Comparison with existing methods demonstrates improved efficiency and practicality.

Abstract

In this paper a homomorphic privacy preserving association rule mining algorithm is proposed which can be deployed in resource constrained devices (RCD). Privacy preserved exchange of counts of itemsets among distributed mining sites is a vital part in association rule mining process. Existing cryptography based privacy preserving solutions consume lot of computation due to complex mathematical equations involved. Therefore less computation involved privacy solutions are extremely necessary to deploy mining applications in RCD. In this algorithm, a semi-trusted mixer is used to unify the counts of itemsets encrypted by all mining sites without revealing individual values. The proposed algorithm is built on with a well known communication efficient association rule mining algorithm named count distribution (CD). Security proofs along with performance analysis and comparison show the well acceptability and effectiveness of the proposed algorithm. Efficient and straightforward privacy model and satisfactory performance of the protocol promote itself among one of the initiatives in deploying data mining application in RCD.