Evaluating Trust Management in Social Networks Using Eviden Theory

Document Type : Original Article

Authors

1 Computer engineering, Faculty of Computer Engineering and Information Technology, Sadjad University of Technology, Mashhad, Iran

2 2Faculty of Computer Engineering and Information Technology, Sadjad University, Mashhad, Iran.

3 Faculty of Computer Engineering and Information Technology, Sadjad University, Mashhad, Iran.

Abstract

Various methods have been presented to evaluate trust management between users in social networks. Individual and subjective diagnosis is less considered in the evaluation of trust and often a general and general model is presented for all users. trust evaluation without considering the personal and mental characteristics of users is not effective. in the proposed method of this research, users' characteristics are calculated and their importance is determined using fuzzy rough set theory. users' characteristics are combined and aggregated by considering their importance and using Dempster Shafer theory of evidence. the sets of trust and distrust values and ambiguity are evaluated to determine the degree of trust. the final values are used to make a trust decision and create a secure connection. the level of user trust in the entire network is determined by all users. the comprehensive performance of the proposed method and RTARS, ABC, DSL-STM and AUTOMATA algorithms have been compared in four evaluation indices and in 10 independent implementations. the obtained results show the improvement of the trust decision and the creation of safe communication in social networks. the proposed algorithm has been able to correctly decide the trust of users in social networks with 92.54% accuracy. the experimental results show that the proposed method is able to infer trust more accurately than the previous methods.

Keywords

Main Subjects

References

[1]. Fatehi, H.S.Shahhoseini, J.Wei, C.T.Chang, "An automata algorithm for generating trusted graphs in online social networks." Applied Soft Computing 118 : 108475, 2022‏.
[2]. Xu, Z.Feng, X.Zhou, M.Xing, H.Wu, X.Xue, L.Qi, “Attention-based neural networks for trust evaluation in online social networks”, Information Sciences, 630, pp.507-522, 2023.
[3]. Fatehi, H.Shahhoseini, ”A hybrid algorithm for evaluating trust in online social networks”, in: 2020 10th International Conference on Computer and Knowledge Engineering, ICCKE(IEEE), pp.158–162, 2020.
[4]. Shakeri, A.Ghaemi Bafghi, “A Layer Model of a Confidence-aware Trust Management System”, International Journal of Information Science and Intelligent System, Vol. 3, pp. 73-90, 2014.
[5]. Alamir, N.Jafari, “Trust Management by Quality of Service & History of Contact in Social Networks”, EFTA, Vol.8, pp.53-66, 2013.
[6]. Cholvy, "A Trust Model in the Logical Belief Function Theory", Journal of Applied Logic 13, no. 4, 441-457, 2015.
[7]. Zhang, S.Zhu, J.Tang, N.Xiong, “A Novel Trust Management Scheme Based on Dempster–Shafer Evidence Theory for Malicious Nodes Detection in Wireless Sensor Networks”, Springer, Volume 74, Issue 4, pp 1779–1801, 2018.
[8]. Zolfaghar, A.Aghaie, “Evolution Of trust Networks in Social Web Applications Using Supervised Learning”, Elsevier Procedia Computer Science, Vol. 3, pp. 833-839, 2011.
[9]. Jiang, G.Wang, J.Wu, “Generating Trusted Graphs for Trust Evaluation in Online Social Networks”, Future generation computer systems 31, pp.48-58. Elsevier, 2014.
[10] Mahajan, Z.Guo, J.H.Cho, I.R.Chen,“Privacy-Preserving and Diversity-Aware Trust-based Team Formation in Online Social Networks”, 2023.
[11] بهشید شایسته، وصال حکمی، سید اکبر مصطفوی، احمد اکبری ازیرانی، «ارائه روشی نوین برای محاسبه اعتماد در کاربردهای اینترنت اشیا»، مجله مهندسی برق دانشگاه تبریز، جلد ۵۰، شماره ۲، صفحات ۷۴۳-۷۵۵، ۱۳۹۹.
[12] مریم فیاض، حامد وحدت نژاد، مهدی خرد، «استنتاج اعتماد در شبکه‌های اجتماعی با ترکیب شبکه عصبی و الگوریتم ژنتیک»، مجله مهندسی برق دانشگاه تبریز، جلد ۵۰، شماره ۱، صفحات ۳۳۱-۳۴۰، ۱۳۹۹.
[13] Qiu, L.Zhang,S.Wang, “A Trust Transitivity Model Based-on Dempster-Shafer Theory”, Journal of Networks,Vol. 5, No. 9, 2010.
[14] VS, M.MSK, “Efficient trust management with Bayesian Evidence theorem to secure public key infrastructure-based mobile ad hoc networks”, EURASIP Journal on Wireless Communications and Networking, pp.1-27, 2018.
[15] Abdelghani, I.Amous, C.A.Zayani, F.Sèdes, G.Roman-Jimenez, "Dynamic and scalable multi-level trust management model for Social Internet of Things".The Journal of Supercomputing, 78(6), 8137-8193, 2022.
[16] Amiri-Zarandi, R.A.Dara, E.Fraser, “LBTM: A lightweight blockchain-based trust management system for social internet of things”. The Journal of Supercomputing, pp.1-19, 2022.
[17] Ma, “Towards Effective Genetic Trust Evaluation in Open Network”, IEEE 20th International Conference on High Performance Computing and Communications, 2018.
[18] Ahmadian, M.Afsharchi, M. Meghdadi, "an effective social recommendation method based on user reputation model and rating profile enhancement." Journal of Information Science, pp. 1–36, 2018.
[19] Mo, T.Wang, S.Zhang, J.Zhang, “An active and verifiable trust evaluation approach for edge, computing”, Journal of Cloud computing Advances, Systems and Applications, pp.1-19, 2020.
[20] U.Nasir, T.H.Kim, “Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation”, IEEE Access, Vol 8, pp.41362-41371, 2020.
[21] Wang, X.Tong, “Trust Prediction Based on Extreme Learning Machine and Asymmetric Tri-Training”, IEEE Access, pp.64358-64367, Vol 9, 2021.
[22] Saeidi, "A new model for calculating the maximum trust in Online Social Networks and solving by Artifcial Bee Colony algorithm." Computational Social Networks, Vol 7, No, 3, 2020.
[23] Wu, L.Tian, Y.Zhang, Z.Wang, “Web User Trust Evaluation: A Novel Approach Using Fuzzy Petri Net and Behavior Analysis”, MDPI, Symmetry Journal, No 13, p.1487, 2021.
[24] Zhang, J.Li, Q.Sun, A.Zhou, “A Comprehensive and Efficient Trust Evaluation Framework for Distributed Networks”, Journal of Internet Technology, Vol.19, No.7, 2018.
[25] Dubois, H.Prade, “Rough fuzzy sets and fuzzy rough sets”, International Journal of General System, 17(2-3), 191-209, 1990. ‏
[26] Zadeh, “Fuzzy sets”. Information Control, PP.338–353, 1965.
[27] Pawlak, ” Rough sets”, International journal of computer & information sciences, 11, pp.341-356, 1982.
[28] Verbiest, C.Cornelis, F.Herrera, “FRPS: a fuzzy rough prototype selection method”, Pattern Recognit, no.10, pp.2770–2782, 2013.
[29] G.Liu, J. Dezert, Q.Pan, G.Mercier, “Combination of sources of evidence with different discounting factors based on a new dissimilarity measure”. Decis Support Syst NO.52(1),PP.133–141, 2011.
[30] Liu, Q.Pan, J.Dezert, G.Mercier, “Credal c-means clustering method based on belief functions”. Knowl Based Syst 74, PP.119–132, 2015.
[31] G.Liu, Q.Pan, J.Dezert, A. Martin, “Adaptive imputation of missing values for incomplete pattern classification”. Pattern Recognit 52:85–95,2016.

Files

Share

How to cite

Statistics