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Title: Enabling Privacy Policies for mHealth Studies
Pervasive sensing has enabled continuous monitoring of user physiological state through mobile and wearable devices, allowing for large scale user studies to be conducted, such as those found in mHealth. However, current mHealth studies are limited in their ability of allowing users to express their privacy preferences on the data they share across multiple entities involved in a research study. In this work, we present mPolicy, a privacy policy language for study participants to express the context-aware and data-handling policies needed for mHealth. In addition, we provide a privacy-adaptive policy creation mechanism for byproduct data (such as motion inferences). Lastly, we create a software library called privLib for implementing parsing, enforcement, and policy creation on byproduct data for mPolicy. We evaluate the latency overhead of these operations, and discuss future improvements for scaling to realistic mHealth scenarios.
Authors:
;
Award ID(s):
1636916 1640813 1822935
Publication Date:
NSF-PAR ID:
10149622
Journal Name:
2019 IEEE International Conference on Big Data (Big Data)
Page Range or eLocation-ID:
4045 to 4054
Sponsoring Org:
National Science Foundation
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