Privacy Preserving Association Rule Mining Revisited

TL;DR

This paper revisits a privacy-preserving association rule mining scheme, analyzing its limitations, proposing a more robust privacy definition, and introducing a hybrid scheme that improves efficiency and privacy guarantees.

Contribution

It introduces a new hybrid scheme for privacy-preserving association rule mining that addresses storage, computation, and privacy limitations of previous methods.

Findings

The proposed scheme is more efficient than previous methods.

It offers a more robust privacy guarantee considering average case privacy.

Experimental results demonstrate improved performance and privacy protection.

Abstract

The privacy preserving data mining (PPDM) has been one of the most interesting, yet challenging, research issues. In the PPDM, we seek to outsource our data for data mining tasks to a third party while maintaining its privacy. In this paper, we revise one of the recent PPDM schemes (i.e., FS) which is designed for privacy preserving association rule mining (PP-ARM). Our analysis shows some limitations of the FS scheme in term of its storage requirements guaranteeing a reasonable privacy standard and the high computation as well. On the other hand, we introduce a robust definition of privacy that considers the average case privacy and motivates the study of a weakness in the structure of FS (i.e., fake transactions filtering). In order to overcome this limit, we introduce a hybrid scheme that considers both privacy and resources guidelines. Experimental results show the efficiency of our proposed scheme over the previously introduced one and opens directions for further development.