Start Date
16-8-2018 12:00 AM
Description
Internet of Things (IoT) is considered one of the emerging technologies in the area of information technology. However, many challenges still need to be overcome, one of which is privacy. The constant collection of information by IoT devices can bring serious problems to its users' privacy. In some scenarios, users are unaware that the information shared can bring such risks. In this context, this work aims to present a privacy preservation mechanism in IoT environments. This mechanism aims to mediate the exchange of information in these environments to provide users with the ability to control the disclosure of their data. To validate the mechanism, an experiment was made with the users, in which they were underwent to several scenarios that could occur in IoT environments. The results of the mechanism learning process obtained an accuracy rate of 88.62% in the predicted scenarios. In addition, the mechanism presents the essential characteristics to help users preserve their privacy.
Recommended Citation
Pereira Couto, Fagner Roger and Zorzo, Sergio, "Privacy Negotiation Mechanism in Internet of Things Environments" (2018). AMCIS 2018 Proceedings. 33.
https://aisel.aisnet.org/amcis2018/Security/Presentations/33
Privacy Negotiation Mechanism in Internet of Things Environments
Internet of Things (IoT) is considered one of the emerging technologies in the area of information technology. However, many challenges still need to be overcome, one of which is privacy. The constant collection of information by IoT devices can bring serious problems to its users' privacy. In some scenarios, users are unaware that the information shared can bring such risks. In this context, this work aims to present a privacy preservation mechanism in IoT environments. This mechanism aims to mediate the exchange of information in these environments to provide users with the ability to control the disclosure of their data. To validate the mechanism, an experiment was made with the users, in which they were underwent to several scenarios that could occur in IoT environments. The results of the mechanism learning process obtained an accuracy rate of 88.62% in the predicted scenarios. In addition, the mechanism presents the essential characteristics to help users preserve their privacy.