Artificial Intelligence and Machine Learning based Ambient Assisted Living systems play an important role in smart cities by improving the quality of life of the elderly population. Many Ambient Assisted Living systems are coupled with Android Apps for command-and-control purposes. Consequently, the privacy and security of Ambient Assisted Living systems depend on the privacy and security of the corresponding Android Apps, which follow a privacy self-management model. Unfortunately, the privacy self-management model ignores the decision-making abilities of the elderly and increases their cognitive loads, which put their privacy protection and wellbeing at stake. In this paper, we follow a Human-Centered Artificial Intelligence inspired approach for addressing these issues. This approach uses privacy as a shared responsibility model instead of the privacy self-management model. We have proposed two algorithms, the participatory privacy protection algorithm-I, and participatory privacy protection algorithm-II, for determining optimal privacy settings of an Ambient Assisted Living App and handling its runtime Permission requests, respectively. We demonstrated the working of these algorithms using a case study. We have also compared the proposed approach with state-of-the-art privacy management schemes for Android Apps. The proposed algorithms can improve the privacy protection of Ambient Assisted Living App users in smart cities and relieve them through cognitive offloading.

A human-centered artificial intelligence approach for privacy protection of elderly App users in smart cities

Castiglione A.;
2021-01-01

Abstract

Artificial Intelligence and Machine Learning based Ambient Assisted Living systems play an important role in smart cities by improving the quality of life of the elderly population. Many Ambient Assisted Living systems are coupled with Android Apps for command-and-control purposes. Consequently, the privacy and security of Ambient Assisted Living systems depend on the privacy and security of the corresponding Android Apps, which follow a privacy self-management model. Unfortunately, the privacy self-management model ignores the decision-making abilities of the elderly and increases their cognitive loads, which put their privacy protection and wellbeing at stake. In this paper, we follow a Human-Centered Artificial Intelligence inspired approach for addressing these issues. This approach uses privacy as a shared responsibility model instead of the privacy self-management model. We have proposed two algorithms, the participatory privacy protection algorithm-I, and participatory privacy protection algorithm-II, for determining optimal privacy settings of an Ambient Assisted Living App and handling its runtime Permission requests, respectively. We demonstrated the working of these algorithms using a case study. We have also compared the proposed approach with state-of-the-art privacy management schemes for Android Apps. The proposed algorithms can improve the privacy protection of Ambient Assisted Living App users in smart cities and relieve them through cognitive offloading.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4810903
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