A holistic model for security of learning applications in smart cities

Abstract

Modern learning frameworks take advantage of the interconnection among individuals, multimedia artifacts, places, events, and physical objects. In this perspective, smart cities are primary providers of data, learning stimuli and realistic hands-on laboratories. Unfortunately, the development of smart-city-enabled learning frameworks leads to many privacy and security risks since they are built on top of IoT nodes, wireless sensors networks and cyber-physical systems. To efficiently address such issues, a suitable holistic approach is needed, especially to reveal the interdependence between different actors, e.g., cloud infrastructures, resource-constrained devices and big data sources. Therefore, this paper introduces a model to help the engineering of novel learning frameworks for smart cities by enlightening the problem space characterizing security.

Keywords

• e-Learning
• Smart Cities
• Big Data
• Privacy and Security
• Model Driven Design