Privacy-preserving mHealth Data Release with Pattern Consistency

TL;DR

This paper introduces a novel privacy-preserving data release method for mHealth systems that maintains data pattern integrity while ensuring differential privacy, validated through real data experiments.

Contribution

It presents a new bucket partition algorithm combined with differential privacy to preserve data patterns in mHealth data releases, outperforming existing methods.

Findings

The approach achieves differential privacy for mHealth data.

The partitioning algorithm outperforms state-of-the-art in pattern preservation by 1.75 times.

Extensive simulations validate the accuracy and effectiveness of the method.

Abstract

Mobile healthcare system integrating wearable sensing and wireless communication technologies continuously monitors the users' health status. However, the mHealth system raises a severe privacy concern as the data it collects are private information, such as heart rate and blood pressure. In this paper, we propose an efficient and privacy-preserving mHealth data release approach for the statistic data with the objectives to preserve the unique patterns in the original data bins. The proposed approach adopts the bucket partition algorithm and the differential privacy algorithm for privacy preservation. A customized bucket partition algorithm is proposed to combine the database value bins into buckets according to certain conditions and parameters such that the patterns are preserved. The differential privacy algorithm is then applied to the buckets to prevent an attacker from being able to identify the small changes at the original data. We prove that the proposed approach achieves differential privacy. We also show the accuracy of the proposed approach through extensive simulations on real data. Real experiments show that our partitioning algorithm outperforms the state-of-the-art in preserving the patterns of the original data by a factor of 1.75.