Security and safety intelligent systems on smart spaces: privacy perseveration approach

We are witnessing the rapid development of the Internet of Things (IoT) which provides ubiquitous sensing and computing capabilities to connect a broad range of things to the Internet while connecting people and spaces. That opens a wide range of opportunities to foster user-centered service, improve safety, and well-being. Intelligent IoT-based solutions have been shown to have significant potential in people safety and risk environment monitoring, due to their ability to operate at a fine granular level and provide rich low-level information.
To obtain mean-full insights into data generated from ubiquitous IoT devices in smart spaces, artificial intelligence (AI) techniques have been widely exploited to train data models for enabling intelligent IoT applications. Traditionally, data is sensed and communicated to data analytics and AI functions which are placed in a cloud server or a data center for data learning and modeling. This incurs critical limitations, such as the offloading of massive IoT data to the remote servers may be infeasible due to the required network resources and the incurred latency. The use of third-party servers for AI training also raises privacy concerns such as data breaches as the training data may contain sensitive information and also open extended security challenges.

Faculty Supervisor:

Fehmi Jaafar;Mohamed Cheriet;Darine Ameyed

Student:

Partner:

Quartier de l'innovation de Montréal

Discipline:

Computer science

Sector:

Information and Communications Technology; Technology; Other

University:

Université du Québec à Chicoutimi

Program: