Mitigating the ICA Attack against Rotation Based Transformation for Privacy Preserving Clustering

TL;DR

This paper proposes a modified rotation based transformation method to defend against ICA attacks in privacy-preserving clustering, enhancing data security while retaining the benefits of the original approach.

Contribution

It introduces a novel MRBT technique that mitigates ICA attacks specifically for privacy-preserving clustering applications, improving security without sacrificing utility.

Findings

MRBT effectively resists ICA attacks

Maintains data utility for clustering tasks

Enhances privacy in data mining

Abstract

The rotation based transformation (RBT) for privacy preserving data mining (PPDM) is vulnerable to the independent component analysis (ICA) attack. This paper introduces a modified multiple rotation based transformation (MRBT) technique for special mining applications mitigating the ICA attack while maintaining the advantages of the RBT.