Authors: Gang Wang; Jaehong Park; Ravi Sandhu; Jun Wang; Xiaolin Gui
Addresses: Department of Computer Science and Technology, Xi'an University of Finance and Economics, No. 2 Weichang Road, Chang'an District, Xi'an, 710100, China; Institute for Cyber Security, University of Texas at San Antonio, NPB 3.122, UTSA Main Campus, San Antonio, TX 78249, Texas, USA ' College of Business Administration, University of Alabama in Huntsville, 301 Sparkman Drive, Huntsville, AL 35899, USA ' Institute for Cyber Security, University of Texas at San Antonio, NPB 3.122, UTSA Main Campus, San Antonio, TX 78249, Texas, USA ' Department of Electronic Commerce, Xi'an University of Finance and Economics, No. 2 Weichang Road, Chang'an District, Xi'an, 710100, China ' Department of Electronics and Information Engineering, Xi'an Jiaotong University, No. 28 Xian ning Road, Xi'an, 710049, China
Abstract: Mutual trust is the most important basis in social networks. However, many malicious nodes often deceive, collaboratively cheat, and maliciously recommend other nodes for getting the more benefits. Meanwhile, because of lacking effective incentive strategy, many nodes are neither to evaluate nor to recommend. Thus, malicious actions have been aggravated in social networks. To solve these issues, we designed a bidding strategy to incentivise nodes to do their best to recommend or evaluate service node. At the same time, we also employed TOPSIS method of selecting a correct service node for system from networks. To guarantee reliability of service node selected, we brought recommendation time influential function, service content similarity function and recommendation acquaintance function into the model to compute general trust of node. Finally, we gave an update method for trust degree of node and experiments analysis.
Keywords: dynamic trust; trust evaluation model; bid; multi-attributes; TOPSIS; information entropy; recommendation trust; direct trust; Markov chain.
International Journal of High Performance Computing and Networking, 2019 Vol.13 No.4, pp.436 - 454
Received: 04 Jul 2016
Accepted: 18 Oct 2016
Published online: 16 Apr 2019 *