  Wearable devices generate different types of physiological data about the
individuals. These data can provide valuable insights for medical researchers
and clinicians that cannot be availed through traditional measures. Researchers
have historically relied on survey responses or observed behavior.
Interestingly, physiological data can provide a richer amount of user cognition
than that obtained from any other sources, including the user himself.
Therefore, the inexpensive consumer-grade wearable devices have become a point
of interest for the health researchers. In addition, they are also used in
continuous remote health monitoring and sometimes by the insurance companies.
However, the biggest concern for such kind of use cases is the privacy of the
individuals. There are a few privacy mechanisms, such as abstraction and
k-anonymity, are widely used in information systems. Recently, Differential
Privacy (DP) has emerged as a proficient technique to publish privacy sensitive
data, including data from wearable devices. In this paper, we have conducted a
Systematic Literature Review (SLR) to identify, select and critically appraise
researches in DP as well as to understand different techniques and exiting use
of DP in wearable data publishing. Based on our study we have identified the
limitations of proposed solutions and provided future directions.
