A risk evaluation approach for authorization decisions in social pervasive applications. (October 2016)

• Record Type: Journal Article
• Authors: Ali-Eldin, Amr; van den Berg, Jan; Ali, Hesham A.
• Is Part Of: Computers & electrical engineering. Volume 55(2016)
• Page Start: 59
• Page End: 72
• Publication Date: 2016-10
• Subjects: Security risks -- Authorization -- Social networks -- Social Network Services (SNS) -- Social pervasive applications -- Neuro-fuzzy systems; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.01.022
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A new risk indicator model to evaluate security risks associated with information sharing on social pervasive applications. A risk indicator value can be predicted by assessing a number of risk attributes using a neurofuzzy technique. The proposed model was implemented successfully in a case study at a university campus. An effective approach in predicting security risks and in deciding up on them about disclosure decisions in social pervasive applications. Abstract: Most research in social networks has focused on the assumption that unknown entities are malicious and thus the traditional approach was to detect them and deny their access to sensitive data. In this paper, we propose a new computational model that helps users predict security risks associated with their information sharing on social networks. The model is based on the assumption that a risk indicator value can be predicted by assessing a number of risk attributes using a neuro-fuzzy technique. A disclosure decision is made based on this risk indicator value. The approach was tested in a real prototype of a social mobile service at a university campus. Further, we show how the model can be implemented in a popular social rating site. Results obtained show the relevance and effectiveness of the proposed approach in predicting risks and in deciding up on it about disclosure decisions in social pervasive applications. Graphical abstract: