  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.
