Evaluating Privacy Policy for Mobile Health APPs with Machine Learning

[Objective] This paper analyzes privacy policies for mobile health APPs in China with machine learning, aiming to improve the efficiency and accuracy of compliance evaluation. [Methods] First, we constructed the evaluation system for the privacy policy compliance of mobile health APPs according to relevant policies and regulations. Then, based on the hard voting classifier, we established the compliance evaluation model integrating three machine learning algorithms: CNN, RNN and LSTM. Finally, we examined our model using 1210 mobile health APPs from the Android APP market, and evaluated the compliance of their privacy policies. [Results] The overall compliance of the privacy policies for mobile health APPs was poor. There are many violations in the six evaluation criteria. The compliance scores of online medical APPs, medical service APPs, health management APPs, and medical information APPs were 0.63, 0.59, 0.61and 0.66. [Limitations] Due to the limited amount of annotated privacy policy data, the proposed model may not be able to fully learn the features of evaluation indicators. [Conclusions] This proposed model could conduct large-scale, fine-grained automatic evaluation of the compliance of APPs privacy policies. It also provides new ideas and methods for the government agencies and APP operators to improve decision making. © 2022, Chinese Academy of Sciences. All rights reserved.