Visible to the public Trustworthiness Inference Framework in the Social Internet of Things: A Context-Aware Approach

TitleTrustworthiness Inference Framework in the Social Internet of Things: A Context-Aware Approach
Publication TypeConference Paper
Year of Publication2019
AuthorsXia, H., Xiao, F., Zhang, S., Hu, C., Cheng, X.
Conference NameIEEE INFOCOM 2019 - IEEE Conference on Computer Communications
Keywordscomposability, context-aware approach, context-aware trustworthiness inference framework, direct trust, external similarity trust, familiarity trust, Fuzzy logic, fuzzy logic method, grey systems, human behaviors, internal similarity trust, Internet of Things, kernel-based nonlinear multivariate grey prediction, kernel-based nonlinear multivariate grey prediction model, Predictive models, Protocols, psychological principles, psychology, pubcrawl, recommendation trust, reliability, smart objects, social Internet of Things, social networking, social networking (online), sociological principles, trust elements, trust generation, Trust management, Trusted Computing, trustworthiness, trustworthiness inference framework
AbstractThe concept of social networking is integrated into Internet of things (IoT) to socialize smart objects by mimicking human behaviors, leading to a new paradigm of Social Internet of Things (SIoT). A crucial problem that needs to be solved is how to establish reliable relationships autonomously among objects, i.e., building trust. This paper focuses on exploring an efficient context-aware trustworthiness inference framework to address this issue. Based on the sociological and psychological principles of trust generation between human beings, the proposed framework divides trust into two types: familiarity trust and similarity trust. The familiarity trust can be calculated by direct trust and recommendation trust, while the similarity trust can be calculated based on external similarity trust and internal similarity trust. We subsequently present concrete methods for the calculation of different trust elements. In particular, we design a kernel-based nonlinear multivariate grey prediction model to predict the direct trust of a specific object, which acts as the core module of the entire framework. Besides, considering the fuzziness and uncertainty in the concept of trust, we introduce the fuzzy logic method to synthesize these trust elements. The experimental results verify the validity of the core module and the resistance to attacks of this framework.
Citation Keyxia_trustworthiness_2019