We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Trust Aware System for Social Networks: A Comprehensive Survey

by Manasa S. M., Manjula S. H., Venugopal K. R.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 5
Year of Publication: 2017
Authors: Manasa S. M., Manjula S. H., Venugopal K. R.
10.5120/ijca2017913307

Manasa S. M., Manjula S. H., Venugopal K. R. . Trust Aware System for Social Networks: A Comprehensive Survey. International Journal of Computer Applications. 162, 5 ( Mar 2017), 34-43. DOI=10.5120/ijca2017913307

@article{ 10.5120/ijca2017913307,
author = { Manasa S. M., Manjula S. H., Venugopal K. R. },
title = { Trust Aware System for Social Networks: A Comprehensive Survey },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 5 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 34-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume162/number5/27242-2017913307/ },
doi = { 10.5120/ijca2017913307 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:08:13.057666+05:30
%A Manasa S. M.
%A Manjula S. H.
%A Venugopal K. R.
%T Trust Aware System for Social Networks: A Comprehensive Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 5
%P 34-43
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Social networks are the platform for the users to get connected with other social network users based on their interest and life styles. Existing social networks have millions of users and the data generated by them are huge and it is difficult to differentiate the real users and the fake users. Hence a trust worthy system is recommended for differentiating the real and fake users. Social networking enables users to send friend requests, upload photos and tag their friends and even suggest them the web links based on the interest of the users. The friends recommended, the photos tagged and web links suggested may be a malware or an untrusted activity. Users on social networks are authorised by providing the personal data. This personal raw data is available to all other users online and there is no protection or methods to secure this data from unknown users. Hence to provide a trustworthy system and to enable real users activities a review on different methods to achieve trustworthy social networking systems are examined in this paper.

References
  1. D. L. Iglesias, J.-M. Marques, G. Cabrera, H. Rifa-Pous, and A. Mon- tane, “Hornet: Microblogging for a Contributory Social Network,” IEEE Internet Computing, vol. 16, no. 3, pp. 37–45, 2012.
  2. X. Liang, K. Zhang, X. Shen, and X. Lin, “Security and Privacy in Mobile Social Networks: Challenges and Solutions,” IEEE Wireless Communications, vol. 21, no. 1, pp. 33–41, 2014.
  3. B. Li, L. Liao, H. Leung, and R. Song, “PHAT: A Preference and Honesty Aware Trust Model for Web Services,” IEEE Transactions on Network and Service Management, vol. 11, no. 3, pp. 363–375, 2014.
  4. M. Eirinaki, M. D. Louta, and I. Varlamis, “A Trust-Aware System for Personalized User Recommendations in Social Networks,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 44, no. 4, pp. 409– 421, 2014.
  5. G.Vasanthakumar,P.D.Shenoya,andK.R.Venugopal,“PTIB:Profiling Top Influential Blogger in Online Social Networks,”
  6. I. Ivanov, P. Vajda, P. Korshunov, and T. Ebrahimi, “Comparative Study of Trust Modeling for Automatic Landmark Tagging,” IEEE Transactions on Information Forensics and Security, vol. 8, no. 6, pp. 911–923, 2013.
  7. S. Caton, C. Haas, K. Chard, K. Bubendorfer, and O. F. Rana, “A Social Compute Cloud: Allocating and Sharing Infrastructure Resources via Social Networks,” IEEE Transactions on Services Computing, vol. 7, no. 3, pp. 359–372, 2014.
  8. L. Yang, F. Hao, S. Li, G. Min, H. Kim, and S. Yau, “An Efficient Approach to Generating Location-Sensitive Recommendations in Ad- hoc Social Network Environments,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 11, pp. 2944–2955, 2014.
  9. G. Liu, Y. Wang, M. A. Orgun, and E.-P. Lim, “Finding the Optimal Social Trust Path for the Selection of Trustworthy Service Providers in Complex Social Networks,” IEEE Transactions on Services Computing, vol. 6, no. 2, pp. 152–167, 2013.
  10. N. Z. Gong and D. Wang, “On the Security of Trustee-Based Social Authentications,” IEEE Transactions on Information Forensics and Se- curity, vol. 9, no. 8, pp. 1251–1263, 2014.
  11. K. R. Venugopal, K. Srinivasa, and L. M. Patnaik, Soft Computing for Data Mining Applications. Springer, 2009.
  12. M. Gjoka, C. T. Butts, M. Kurant, and A. Markopoulou, “Multigraph Sampling of Online Social Networks,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 9, pp. 1893–1905, 2011.
  13. N. Laranjeiro, M. Vieira, and H. Madeira, “A Technique for Deploying Robust Web Services,” IEEE Transactions on Services Computing, vol. 7, no. 1, pp. 68–81, 2014.
  14. R. Jia, K. Zheng, J. Zhang, L. Fu, P. Du, X. Wang, and J. Xu, “Asymptotic Analysis on Throughput and Delay in Cognitive Social Networks,” IEEE Transactions on Communications, vol. 62, no. 8, pp. 2721–2732, 2014.
  15. H. C. Chu, D. J. Deng, and J. H. Park, “Live Data Mining Concerning Social Networking Forensics based on a Facebook Session through Aggregation of Social Data,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 7, pp. 1368–1376, 2011.
  16. S. Deng, L. Huang, G. Xu, X. Wu, and Z. Wu, “On Deep Learning for Trust-Aware Recommendations in Social Networks,” IEEE Transactions on Neural Networks and Learning Systems, 2016.
  17. P. D. Shenoy, K. Srinivasa, K. R. Venugopal, and L. M. Patnaik, “Dynamic Association Rule Mining using Genetic Algorithms,” Intelligent Data Analysis, vol. 9, no. 5, pp. 439–453, 2005.
  18. P. Wang, Z. Ding, C. Jiang, and M. Zhou, “Constraint-Aware Approach to Web Service Composition,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 44, no. 6, pp. 770–784, 2014.
  19. W. Chen, I. Paik, and P. C. Hung, “Constructing a Global Social Service Network for Better Quality of Web Service Discovery,” IEEE Transactions on Services Computing, vol. 8, no. 2, pp. 284–298, 2015.
  20. L. Liu and H. Jia, “Trust Evaluation via Large-Scale Complex Service- Oriented Online Social Networks,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 11, pp. 1402–1412, 2015.
  21. W. Jiang, J. Wu, F. Li, G. Wang, and H. Zheng, “Trust Evaluation in Online Social Networks Using Generalized Network Flow,” IEEE Transactions on Computers, vol. 65, no. 3, pp. 952–963, 2016.
  22. S. Iftikhar, M. Kamran, E. U. Munir, and S. U. Khan, “A Reversible Watermarking Technique for Social Network Data Sets for Enabling Data Trust in Cyber, Physical, and Social Computing,” IEEE Systems, pp. 1–10, 2015.
  23. G. Vasanthakumar, A. K. Upadhyay, P. F. Kalmath, S. Dinakar, P. D. Shenoy, and K. R. Venugopal, “Up3: User Profiling from Profile Picture in Multi-Social Networking,” in 2015 Annual IEEE India Conference (INDICON), pp. 1–6, IEEE, 2015.
  24. R. Schlegel, C.Y. Chow, Q. Huang, and D. Wong, “Privacy-Preserving Location Sharing Services for Social Networks,” IEEE Transactions on Sevices Computing, pp. 1–14, 2015.
  25. S. Deng, L. Huang, G. Xu, X. Wu, and Z. Wu, “On Deep Learning for Trust-Aware Recommendations in Social Networks,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–13, 2016.
  26. Z. He, Z. Cai, J. Yu, X. Wang, Y. Sun, and Y. Li, “Cost-Efficient Strategies for Restraining Rumor Spreading in Mobile Social Networks,” IEEE Transactions on Vehicular Technology, pp. 1–12, 2016.
  27. F. Hao, S. Li, G. Min, H.C. Kim, S. S. Yau, and L. T. Yang, “An Efficient Approach to Generating Location-Sensitive Recommendations in ad-hoc Social Network Environments,” IEEE Transactions on Services Computing, vol. 8, no. 3, pp. 520–533, 2015.
  28. T. Wang, H. Krim, and Y. Viniotis, “A Generalized Markov Graph Model: Application to Social Network Analysis,” IEEE Journal of Selected Topics in Signal Processing, vol. 7, no. 2, pp. 318–332, 2013.
  29. S. Joshi, V. Simha, D. Shenoy, K. R. Venugopal, and L. Patnaik, “Classification and Treatment of different Stages of Alzheimers Disease using Various Machine Learning Methods,” International Journal of Bioinformatics Research, vol. 2, no. 1, pp. 44–52, 2010.
  30. M. E. Nergiz, E. Cicek, T. Pedersen, and Y. Saygin, “A Look-Ahead Approach to Secure Multi-party Protocols,” IEEE Transactions on Knowledge an d Data Engineering, vol. 24, no. 7, pp. 1170–1185, 2012.
  31. V. M. Prabhakaran and M. M. Prabhakaran, “Assisted Common In- formation with an Application to Secure Two-Party Sampling,” IEEE Transactions on Information Theory, vol. 60, no. 6, pp. 3413–3434, 2014.
  32. N. Laranjeiro, M. Vieira, and H. Madeira, “A Technique for Deploying Robust Web Services,” IEEE Transactions on Services Computing, vol. 7, no. 1, pp. 68–81, 2014.
  33. Z. Li, C. Wang, S. Yang, C. Jiang, and X. Li, “Lass: Local-Activity and Social-Similarity Based Data Forwarding in Mobile Social Networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 1, pp. 174–184, 2015.
  34. H.-P. Yueh, W. Lin, Y.-L. Liu, T. Shoji, and M. Minoh, “The Devel- opment of an Interaction Support System for International Distance Education,” IEEE Transactions on Learning Technologies, vol. 7, no. 2, pp. 191–196, 2014.
  35. L. Guo, C. Zhang, and Y. Fang, “A Trust-Based Privacy-Preserving Friend Recommendation Scheme for Online Social Networks,” IEEE Transactions on Dependable and Secure Computing, vol. 12, no. 4, pp. 413–427, 2015.
  36. X. Qiao, W. Yu, J. Zhang, W. Tan, J. Su, W. Xu, and J. Chen, “Recommending Nearby Strangers Instantly Based on Similar Check-In Behaviors,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 3, pp. 1114–1124, 2015.
  37. M. Doost mohammadian and U. A. Khan, “Graph-Theoretic Distributed Inference in Social Networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 4, pp. 613–623, 2014.
  38. A. Das and M. M. Islam, “SecuredTrust: A Dynamic Trust Computation Model for Secured Communication in Multiagent Systems,” IEEE Trans- actions on Dependable and Secure Computing, vol. 9, no. 2, pp. 261– 274, 2012.
  39. J. Jiang, C. Wilson, X. Wang, W. Sha, P. Huang, Y. Dai, and B. Y. Zhao, “Understanding Latent Interactions in Online Social Networks,” ACM Transactions on the Web (TWEB), vol. 7, no. 4, pp. 1–18, 2013.
  40. F. M. F. Wong, Z. Liu, and M. Chiang, “On the Efficiency of Social Recommender Networks,” in 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 2317–2325, IEEE, 2015.
  41. J. Kwon and S. Kim, “Friend Recommendation Method using Physical and Social Context,” International Journal of Computer Science and Network Security, vol. 10, no. 11, pp. 116–120, 2010.
  42. K. Farrahi and D. Gatica-Perez, “Discovering Routines from Large-Scale Human Locations using Probabilistic Topic Models,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 1, pp. 3–6, 2011.
  43. M. Li, R. Na, Q. Qian, H. Zhu, X. Liang, and L. Yu, “SPFM: Scalable and Privacy-Preserving Friend Matching in Mobile Cloud,” IEEE Internet of Things Journal, 2012.
  44. L. Bian and H. Holtzman, “Online Friend Recommendation through Personality Matching and Collaborative Filtering,” Proc. of UBICOMM, pp. 230–235, 2011.
  45. Z. Wang, J. Liao, Q. Cao, H. Qi, and Z. Wang, “Friendbook: a Semantic- Based Friend Recommendation System for Social Networks,” IEEE Transactions on Mobile Computing, vol. 14, no. 3, pp. 538–551, 2015.
  46. Q. Tang and J. Wang, “Privacy-Preserving Friendship-based Recom- mender Systems,”
  47. S. Deng, L. Huang, G. Xu, X. Wu, and Z. Wu, “On Deep Learning for Trust-Aware Recommendations in Social Networks,” 2016.
  48. S. Huang, J. Zhang, L. Wang, and X.-S. Hua, “Social Friend Recommendation Based on Multiple Network Correlation,” IEEE Transactions on Multimedia, vol. 18, no. 2, pp. 287–299, 2016.
  49. Q. Cao, M. Sirivianos, X. Yang, and T. Pregueiro, “Aiding the Detection of Fake Accounts in Large Scale Social Online Services,” in Presented as part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12), pp. 197–210, 2012.
  50. B. Viswanath, A. Post, K. P. Gummadi, and A. Mislove, “An Analysis of Social Network-based Sybil Defenses,” ACM SIGCOMM Computer Communication Review, vol. 40, no. 4, pp. 363–374, 2010.
  51. K. Thomas, C. Grier, D. Song, and V. Paxson, “Suspended Accounts in Retrospect: An Analysis of Twitter Spam,” in Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, pp. 243–258, ACM, 2011.
  52. G.DanezisandP.Mittal,“Sybilinfer: Detecting SybilNodes using Social Networks.,” in NDSS, San Diego, CA, 2009.
  53. H. Yu, M. Kaminsky, P. B. Gibbons, and A. Flaxman, “Sybilguard: De- fending Against Sybil Attacks via Social Networks,” in ACM SIGCOMM Computer Communication Review, vol. 36, pp. 267–278, ACM, 2006.
  54. H. Yu, P. B. Gibbons, M. Kaminsky, and F. Xiao, “Sybillimit: A Near- Optimal Social Network Defense against Sybil Attacks,” in 2008 IEEE Symposium on Security and Privacy (sp 2008), pp. 3–17, IEEE, 2008.
  55. Z. Yanbin, “Detecting and Characterizing Social Spam Campaigns in Online Social Networks,” 2010.
  56. Z. Yang, C. Wilson, X. Wang, T. Gao, B. Y. Zhao, and Y. Dai, “Uncovering Social Network Sybils in the Wild,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 8, no. 1, pp. 2–8, 2014.
  57. E. K. Asl, J. Bentahar, H. Otrok, and R. Mizouni, “Efficient Community Formation for Web Services,” IEEE Transactions on Services Comput- ing, vol. 8, no. 4, pp. 586–600, 2015.
  58. X. Chen, B. Proulx, X. Gong, and J. Zhang, “Exploiting Social Ties for Cooperative D2D Communications: A Mobile Social Networking Case,” IEEE/ACM Transactions on Networking, vol. 23, no. 5, pp. 1471–1484, 2015.
  59. W. Jiang, J. Wu, G. Wang, and H. Zheng, “Forming Opinions via Trusted Friends: Time-Evolving Rating Prediction using Fluid Dynamics,” IEEE Transactions on Computers, vol. 65, no. 4, pp. 1211–1224, 2016.
  60. N. Zheng, S. Song, and H. Bao, “A Temporal-Topic Model for Friend Recommendations in Chinese Microblogging Systems,” IEEE Trans- actions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 9, pp. 1245–1253, 2015.
  61. J. Parra-Arnau, A. Perego, E. Ferrari, J. Forne, and D. Rebollo- Monedero, “Privacy-Preserving Enhanced Collaborative Tagging,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 1, pp. 180–193, 2014.
  62. G. Vasanthakumar, P. D. Shenoy, and K. R. Venugopal, “Pfu: Profiling Forum Users in Online Social Networks, a Knowledge Driven Data Mining Approach,” in 2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 57–60, IEEE, 2015.
  63. K.A. Shim, “An Efficient Conditional Privacy-Preserving Authentication Scheme for Vehicular Sensor Networks,” IEEE Transactions on Vehicular Technology, vol. 61, no. 4, pp. 1874–1883, 2012.
  64. E. Miluzzo, N. D. Lane, S. B. Eisenman, and A. T. Campbell, “Cenceme–Injecting Sensing Presence into Social Networking Applications,” in European Conference on Smart Sensing and Context, pp. 1–28, Springer, 2007.
  65. Y. Altshuler, E. Shmueli, G. Zyskind, O. Lederman, N. Oliver, and A. Pentland, “Campaign Optimization through Behavioral Modeling and Mobile Network Analysis,” IEEE Transactions on Computational Social Systems, vol. 1, no. 2, pp. 121–134, 2014.
  66. S. Wen, M. S. Haghighi, C. Chen, Y. Xiang, W. Zhou, and W. Jia, “A Sword with Two Edges: Propagation Studies on both Positive and Negative Information in Online Social Networks,” IEEE Transactions on Computers, vol. 64, no. 3, pp. 640–653, 2015.
  67. B. Carminati, E. Ferrari, and A. Perego, “Enforcing Access Control in Web-Based Social Networks,” ACM Transactions on Information and System Security (TISSEC), vol. 13, no. 1, pp. 1–6, 2009.
  68. P. Lin, P.-C. Chung, and Y. Fang, “P2P-isn: A Peer-to-Peer Architecture for Heterogeneous Social Networks,” IEEE Transactions on Network, vol. 28, no. 1, pp. 56–64, 2014.
  69. R.Schlegel, C.Y. Chow, Q. Huang, and D. Wong, “Privacy-Preserving Location Sharing Services for Social Networks,” IEEE Transactions on Services Computing, vol. 99, no. 1, pp. 1–11, 2016.
  70. R. Agrawal, J. Kiernan, R. Srikant, and Y. Xu, “Order Preserving En- cryption for Numeric Data,” in Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, pp. 563–574, ACM, 2004.
  71. L. Barkhuus, B. Brown, M. Bell, S. Sherwood, M. Hall, and M. Chalmers, “From Awareness to Repartee: Sharing Location within Social Groups,” in proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 497–506, ACM, 2008.
  72. M. Gruteser and D. Grunwald, “Anonymous Usage of Location-Based Services through Spatial and Temporal Cloaking,” in Proceedings of the 1st International Conference on Mobile Systems, Applications and Services, pp. 31–42, ACM, 2003.
  73. L. Siksnys, J. R. Thomsen, S. Saltenis, and M. L. Yiu, “Private and Flexible Proximity Detection in Mobile Social Networks,” in 2010 Eleventh International Conference on Mobile Data Management, pp.75– 84, IEEE, 2010.
  74. E. Toch, J. Cranshaw, P. H. Drielsma, J. Y. Tsai, P. G. Kelley, J. Spring- field, L. Cranor, J. Hong, and N. Sadeh, “Empirical Models of Privacy in Location Sharing,” in Proceedings of the 12th ACM International Conference on Ubiquitous Computing, pp. 129–138, ACM, 2010.
  75. S. Wen, M. S. Haghighi, C. Chen, Y. Xiang, W. Zhou, and W. Jia, “A Sword with Two Edges: Propagation Studies on both Positive and Negative Information in Online Social Networks,” IEEE Transactions on Computers, vol. 64, no. 3, pp. 640–653, 2015.
  76. Y. Liu, S. Xu, and G. Tourassi, “Detecting Rumors Through Modeling Information Propagation Networks in a Social Media Environment,” in International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, pp. 121–130, Springer, 2015.
  77. S. Wen, J. Jiang, Y. Xiang, S. Yu, W. Zhou, and W. Jia, “To Shut Them Up or to Clarify: Restraining the Spread of Rumors in Online Social Networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 12, pp. 3306–3316, 2014.
  78. Y. Zhang, A. Adiga, S. Saha, A. Vullikanti, and B. A. Prakash, “Near- Optimal Algorithms for Controlling Propagation at Group Scale on Networks,” IEEE Transactions on Knowledge and Data Engineering, no. 12, pp. 3339–3352, 2016.
  79. X. Chen, M. Vorvoreanu, and K. Madhavan, “Mining Social Media Data for Understanding Students Learning Experiences,” IEEE Transactions on Learning Technologies, vol. 7, no. 3, pp. 246–259, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Social Networking Sybil Attack Geo-tag