A Data-Driven Framework for Dynamic Trust Management

Reputation and trust-based models have been used extensively in different application domains. These include large online communities such as eBay, Amazon, YouTube and ad-hoc and wireless sensor networks. Recently, the use of the models has gained popularity due to their effectiveness in providing trusted systems or networks. These models focus on online and historical data to determine the reputation of domain members. In this paper, we propose a novel approach for obtaining trust values by focusing not only on online and historical data but also possible future scenarios to anticipate events in the next time intervals. The data-driven framework is able to dynamically obtain and inject data to predict the future trust value of every identity in the system. The advantage of this proactive approach compared to other approaches is that informed decisions about the domain can be made before a compromise occurs.