IM2P-Medical: Towards Individual Management Privacy Preferences for the Medical Web Apps

With the advancement of technology, people are now able to monitor their health more efficiently. Mobile phones and smartwatches are equipped with sensors that can measure real-time changes in blood pressure, SPO2, and other attributes and public them to service providers via web applications (called web apps) for health improvement suggestions. Moreover, users can share the collected health data with other people, such as doctors, relatives, or friends. However, using technology in healthcare has raised the issue of privacy. Some health web apps, by default, intrusively gather and share data. Additionally, smartwatches may monitor people’s health status 24/7. Therefore, users want to control how their health is processed (e.g., collected and shared). This can be cumbersome as they would have to configure each device manually. To address this problem, we have developed a privacy-preference prediction mechanism in the web apps called IM2P-Medical: towards Individual Management Privacy Preferences for the Medical web apps. To capture individual privacy preferences in the web apps, our model learns users’ privacy behavior based on their responses in different medical scenarios. In practice, we exploited several machine learning algorithms: SVM, Gradient Boosting Classifier, Ada Boost Classifier, and Gradient Boosting Regressor. To prove the effectiveness of the proposed model, we set up several scenarios to measure the accuracy as well as the satisfaction level in the two participant groups (i.e., expert and normal users). One key point in this research’s selection of participants is its focus on those living in developing countries, where privacy violation issues are not a common topic. The main contribution of our model is that it allows users to preserve their privacy without configuring privacy settings themselves.

@article{Phien2023,
  title     = {IM2P-Medical: Towards Individual Management Privacy Preferences for the Medical Web Apps},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {4},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Nguyen Ngoc Phien and Nguyen Thi Hoang Phuong and Khiem G. Huynh and Khanh H. Vo and Phuc T. Nguyen and Khoa D. Tran and Bao Q. Tran and Loc C. P. Van and Duy T. Q. Nguyen and Hieu M. Doan and Bang K. Le and Trong D. P. Nguyen and Ngan T. K. Nguyen and Huong H. Luong and Duong Hon Minh},
  doi       = {10.14569/IJACSA.2023.01404100},
  url       = {https://doi.org/10.14569/IJACSA.2023.01404100}
}