Anonymization is an important aspect of data privacy protection, especially in the context of sensitive personal information collected through sensors. In this paper, we propose a new service-based architecture for anonymizing such data in real-time, ensuring that data is accessible to authorized users while maintaining privacy. Our architecture is based on the annotation of data at ingestion time, where privacy levels are assigned to sets of columns. The anonymization procedure is performed by compressing and encoding the data through an autoencoder model, where the encoder and decoder functions are defined as parametric functions composed of multiple hidden layers.

Modern distributed systems consist of a multilayer architecture of IoT, edge, and cloud nodes. Together, they are revolutionizing our lives, bringing intelligence to existing processes (e.g., smart grids) and enabling novel, efficient and effective processes (e.g., remote surgery). This transition however does not come without drawbacks, due to the ever-increasing reliance on devices whose security and safety are, at least, questionable. In this context, research is in its infancy, struggling to adapt successful practices applied, for instance, in cloud systems. Security of modern IoT systems still relies on old-fashioned approaches, mostly static assessments considering only very specific parts of the target system, rather than assessing the system as a whole. In this paper, we put forward the idea of security assurance for IoT, as a higher-level assurance process evaluating the target system at different layers and different moments of its lifecycle, then implemented by a flexible assurance framework. The quality of our approach is evaluated in a real-world smart lighting system.

The conflict between the need of protecting and sharing data is hampering the spread of big data applications. Security and privacy assurance is required to protect data owners, while data access and sharing are fundamental to implement smart big data solutions. In this context, access control systems can assume a central role in balancing data protection and data sharing. However, existing access control solutions are not general and scalable enough to address the software and technological complexity of big data ecosystems, being unable to support such a dynamic and collaborative environment. In this paper, we propose an access control system that enforces access to data in a distributed, multi-party big data environment. It is based on data annotations and secure data transformations performed at ingestion time. We show the feasibility of our approach in the smart city domain using an Apache-based big data engine.