Abstract:
The wide applicability of Internet of Things (IoT) would truly enable the pervasiveness
of smart devices for sensing data. IoT coupled with machine learning
would enter us in an era of smart and personalized, services. In order to achieve
service personalization, there is a need to collect sensitive data about the users.
That yields to privacy concerns due to the possibility of abusing the data or having
attackers to gain unauthorized access. Moreover, the nature of IoT devices,
being resource and computationally constrained, makes it di cult to perform
heavy protection mechanisms. Despite the presence of several solutions for protecting
user privacy, they were not created for the purpose of running on small
devices at a large scale. On top of that, existing solutions lack the customization of user privacy in which users have little to no control over their own private
data. In this regards, we address the aforementioned issue of protecting user's
privacy while taking into account e ciency as well as memory usage. The proposed
scheme embeds an e cient and lightweight algebra based that targets user
privacy and provides e cient policy evaluation. Moreover, an intelligent model to
customize user's privacy based on real time behavior is integrated. Experiments
conducted on synthetic and real-life scenarios to demonstrate the feasibility and
relevance of our proposed framework within IoT environment.
