  Sensors embedded in mobile smart devices can monitor users' activity with
high accuracy to provide a variety of services to end-users ranging from
precise geolocation, health monitoring, and handwritten word recognition.
However, this involves the risk of accessing and potentially disclosing
sensitive information of individuals to the apps that may lead to privacy
breaches. In this paper, we aim to minimize privacy leakages that may lead to
user identification on mobile devices through user tracking and
distinguishability while preserving the functionality of apps and services. We
propose a privacy-preserving mechanism that effectively handles the sensor data
fluctuations (e.g., inconsistent sensor readings while walking, sitting, and
running at different times) by formulating the data as time-series modeling and
forecasting. The proposed mechanism also uses the notion of correlated
noise-series against noise filtering attacks from an adversary, which aims to
filter out the noise from the perturbed data to re-identify the original data.
Unlike existing solutions, our mechanism keeps running in isolation without the
interaction of a user or a service provider. We perform rigorous experiments on
benchmark datasets and show that our proposed mechanism limits user tracking
and distinguishability threats to a significant extent compared to the original
data while maintaining a reasonable level of utility of functionalities. In
general, we show that our obfuscation mechanism reduces the user trackability
threat by 60\% across all the datasets while maintaining the utility loss below
0.5 Mean Absolute Error (MAE). We also observe that our mechanism is more
effective in large datasets. For example, with the Swipes dataset, the
distinguishability risk is reduced by 60\% on average while the utility loss is
below 0.5 MAE.
