The growing intelligence and popularity of smartphones and the advances in Mobile Ubiquitous Computing have resulted in rapid proliferation of data-sharing applications. Instances of these applications include pervasive social networking, games, file sharing and so on. In such scenarios, users are usually involved in selecting the peers with whom communication should take place, continuously facing trust issues. Unfortunately, providing trust support in a pervasive world is challenging due to peer mobility and lack in central control. We propose a novel approach that establishes trust leveraging users' profiles: humans today produce rich strings of unique data twenty-four hours a day. These information enables a task-Aware trust model, namely a finer-grained model in which users are classified as trusted or not depending on the intended business activity. However, simply collecting user's interests may be insufficient to provide a reasonable trust management system. In order to enable the system to recognize malicious users, we include a recommendation subsystem based on the Wilson score confidence interval. It has been designed to be lightweight, minimizing battery depletion. It also protects user privacy. To make our approach fully deployable, it supports two modalities: A TPM-based one and a TPM-less one. The former gives more security guarantees and ensures a fully distributed approach. The latter, requires a Trusted Authority to avoid feedbacks to get tampered and is no more fully distributed. © 2013 IEEE.
FeelTrust: Providing trustworthy communications in Ubiquitous Mobile environment
Castiglione, Aniello;Fiore, Ugo;
2013-01-01
Abstract
The growing intelligence and popularity of smartphones and the advances in Mobile Ubiquitous Computing have resulted in rapid proliferation of data-sharing applications. Instances of these applications include pervasive social networking, games, file sharing and so on. In such scenarios, users are usually involved in selecting the peers with whom communication should take place, continuously facing trust issues. Unfortunately, providing trust support in a pervasive world is challenging due to peer mobility and lack in central control. We propose a novel approach that establishes trust leveraging users' profiles: humans today produce rich strings of unique data twenty-four hours a day. These information enables a task-Aware trust model, namely a finer-grained model in which users are classified as trusted or not depending on the intended business activity. However, simply collecting user's interests may be insufficient to provide a reasonable trust management system. In order to enable the system to recognize malicious users, we include a recommendation subsystem based on the Wilson score confidence interval. It has been designed to be lightweight, minimizing battery depletion. It also protects user privacy. To make our approach fully deployable, it supports two modalities: A TPM-based one and a TPM-less one. The former gives more security guarantees and ensures a fully distributed approach. The latter, requires a Trusted Authority to avoid feedbacks to get tampered and is no more fully distributed. © 2013 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.