Book Details

Design and Implementation of an Online Social Network Application with Privacy Violation Detector Based On Text Mining Technique

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Download this PDF format


Now a days Online Social Networks [OSN] are integral part of human society. In the present scenario of Networked Society, a person maintains an account for social networks for building interpersonal relations, business exchanges/transactions, sharing of ideas and information among like-mined peoples for various activities. Because of its enormous benefits, the use of OSN’s is increasing day by day. The users of such systems are badly in need of the facilities for protection of their private/personal information and their likes and dislikes. Here, the term privacy is defined as: it is the maintenance of confidentiality. To keep secrecy of personal information of users and prevent privacy violation, various techniques are available and supported by different OSNs. However, the existing system could not fulfill privacy maintenance as the expected level of users. This paper discusses a model that allows user to take over the control of their profiles in OSN and thereby control the privacy. The key functions of the proposed model are that the message posted on user wall’s will be filtered for unwanted content in any form and type and will be posted with the users consent only. The feasibility of this model is considered with the present OSN’s scenarios.


[1] C. G. Akcora, B. Carminati, and E. Ferrari, “Risks of friendships on social networks,” in IEEE International Conference on Data Mining (ICDM), 2012, pp. 810–815.

[2] K. Liu and E. Terzi, “A framework for computing the privacy scores of users in online social networks,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 5, no. 1, pp. 6:1–6:30, 2010.

[3] L. Fang and K. LeFevre, “Privacy wizards for social networking sites,” in Proceedings of the 19th international conference on World Wide Web.ACM, 2010, pp. 351–360.

[4] A. C. Squicciarini, D. Lin, S. Sundareswaran, and J. Wede, “Privacy policy inference of user-uploaded images on content sharing sites,” IEEE Trans. Knowl. Data Eng., vol. 27, no. 1, pp. 193–206, 2015.


Privacy Violation, Online Social Networks, Information Filtering, Text Mining.

  • Format Volume 5, Issue 2, No 01, 2017
  • Copyright All Rights Reserved ©2017
  • Year of Publication 2017
  • Author V. Ramya, S.Baskaran
  • Reference IJCS-302
  • Page No 2020-2026

Copyright 2022 SK Research Group of Companies. All Rights Reserved.