Safe sonet: a framework for building trustworthy relationships
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2018-05-07 https://doi.org/10.14419/ijet.v7i2.26.12535 -
SNS Security, Transliteration, Machine Learning, Trust Modeling, Recommender. -
Abstract
The advent of easy to use services and the ability to bridge boundaries in space and time has increasingly changed our social lives and com-munication habits. Being unaware of their audience people on social networks inconsiderately share many personal items. Generally Social Networking Sites (SNS) users depend on the SNS platform for managing their social identities. Although SNS providers have introduced privacy settings to protect users against threats, these are insufficient and the access control models are difficult to be understood by novice users [1]. Unexpectedly, even those who are aware of the security and privacy threats and the preventive tools that combat those threats, lack the motivation to utilize security features to protect themselves. The paper discusses a frame-work (Safe SoNet) that aims to provide a plat-form for secure sharing of posts. The objective of this framework is twofold. Firstly, to analyze user behavior by retrieving user information from various social networking sites, addressing transliteration issues. Secondly, apply the user behavior to quantify the trust score of a connect, which would in turn form a decision making parameter to decide on the peer with whom the information can be securely shared.
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References
[1] Ho, Ai, AbdouMaiga, and EsmaAïmeur. "Privacy protection issues in social networking sites." Computer Systems and Applications, 2009. AICCSA 2009. IEEE/ACS International Conference on. IEEE, 2009.
[2] Yang, Li, et al. "Research of Security Relationship Based on Social Networks." International Conference on Trustworthy Computing and Services. Springer, Berlin, Heidelberg, 2012.
[3] Liu, Huiqing, Jinyan Li, and Limsoon Wong. "A comparative study on feature selec-tion and classication methods using gene expression pro les and proteomic patterns.†Genome informatics 13, 51-60, (2002).
[4] Huang, Lung-Cheng, Sen-Yen Hsu, and Eugene Lin. "A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data." Journal of Translational Medicine 7.1 (2009): 81.
[5] Huang, Jin, Jingjing Lu, and Charles X. Ling. "Comparing naive Bayes, decision trees, and SVM with AUC and accuracy." Data Mining, 2003. ICDM 2003. Third IEEE Inter-national Conference on. IEEE, (2003)
[6] Zhang, Chi, et al. "Privacy and security for online social networks: challenges and op-portunities." IEEE Network 24.4 (2010).
[7] Omanakuttan, Saumya, and MadhumitaChatterjee. "Experimental Analysis on Access Control Using Trust Parameter for Social Network." International Conference on Security in Computer Networks and Distributed Systems. Springer, Berlin, Heidelberg, (2014).
[8] Neethu,Harini."Securing image posts in Social networking sites",International Conference On Computational Vision and Bio Inspired Computing",Proceedings of Springer - Lecture Notes in Computational Vision and Biomechanics.,(2017)
[9] Bengio, Yoshua. "Deep learning of representations for unsupervised and transfer learn-ing." Proceedings of ICML Workshop on Unsupervised and Transfer Learning. (2012).
[10] Harini, Narasimhan, and Tattamangalam R. Padmanabhan. "3c-auth: A new scheme for enhancing security." International Journal of Network Security 18.1, 143-150, (2016).
[11] Harini, N. "A System to Screen Posts that Minimize user Frustration." International Journal of Applied Engineering Research 11.6, 3944-3949, (2016).
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How to Cite
MR, N., & Harini, N. (2018). Safe sonet: a framework for building trustworthy relationships. International Journal of Engineering & Technology, 7(2.26), 57-62. https://doi.org/10.14419/ijet.v7i2.26.12535Received date: 2018-05-06
Accepted date: 2018-05-06
Published date: 2018-05-07