Abstract
Usage of online social networks (OSN) has been gaining momentum in the recent past. People with varying cultural background and age, spanning the globe, share their views and day-to-day affairs with their community. There are an abundant number of internal applications as well as third-party applications (TPA) available to the users either through these OSN sites or that which uses the OSNs identity service to login and access the service. When users wish to use the TPA, the user’s data has to be shared with the TPA’s server. Otherwise, the service would be denied. When users share such information with the TPA, firstly, the users may not be aware of the reliability level of TPA and how those TPA would be handling the user’s data. Secondly, they need not provide the promised level of service but still would have acquired the data from the users. Hence, it is necessary to check the TPA’s level of service and the data requested by them before using the service. The reliability inference application (RIA) and application recommender proposed in this work are based on fuzzy inference mechanism. They mimic the human expert’s decision of choosing a TPA. The RIA trades off the risk associated with sharing the data with the level of service offered and renders the reliability score of the applications. The application recommender presents the users with the recommendation for TPA as highly recommended, recommended or recommended with risk based on the user’s privacy preference. It assists the user to choose a TPA that provides the desired level of service matching the user’s privacy preferences.
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Anthonysamy P, Rashid A, Walkerdine J, Greenwood P, Larkou G (2012) Collaborative privacy management for third-party applications in online social networks. In: Proceedings of the 1st workshop on privacy and security in online social media, ACM, New York, NY, USA, PSOSM ’12, pp 5:1–5:4. doi:10.1145/2185354.2185359
Besmer A, Lipford HR, Shehab M, Cheek G (2009) Social applications: exploring a more secure framework. In: Proceedings of the 5th symposium on usable privacy and security, ACM, New York, NY, USA, SOUPS ’09, pp 2:1–2:10. doi:10.1145/1572532.1572535
Chaabane A, Ding Y, Dey R, Kaafar MA, Ross KW (2014) A closer look at third-party osn applications: Are they leaking your personal information? In: Passive and active measurement, Springer, pp 235–246. doi:10.1007/978-3-319-04918-2_23
Cheng Y, Park J, Sandhu RS (2013) Preserving user privacy from third-party applications in online social networks. In: 22nd international world wide web conference, WWW ’13, Rio de Janeiro, Brazil, May 13–17, 2013, Companion Volume, pp 723–728. http://dl.acm.org/citation.cfm?id=2488032
Felt A, Evans D (2008) Privacy protection for social networking apis. In: Proceedings of the workshop on web 2.0 security and privacy, Oakland, CA, pp 1–8
Jsoup (2015) API documentation for jsoup. https://jsoup.org/apidocs/
Kong D, Jin H (2015) Towards permission request prediction on mobile apps via structure feature learning. In: Proceedings of SIAM international conference on data mining (SDM15), SIAM, pp 604–612. doi:10.1137/1.9781611974010
Krutz DE, Mirakhorli M, Malachowsky SA, Ruiz A, Peterson J, Filipski A, Smith J (2015) A dataset of open-source android applications. In: Mining software repositories (MSR), 2015 IEEE/ACM 12th working conference on IEEE, pp 522–525. doi:10.1109/MSR.2015.79
Matlab (2016) Heirachical clustering. https://in.mathworks.com/help/stats/hierarchical-clustering.html
Peng H, Gates C, Sarma B, Li N, Qi Y, Potharaju R, Nita-Rotaru C, Molloy I (2012) Using probabilistic generative models for ranking risks of android apps. In: Proceedings of the 2012 ACM conference on computer and communications security, ACM, New York, NY, USA, CCS ’12, pp 241–252. doi:10.1145/2382196.2382224
Shanmughapriya T, Swamynathan S (2016) An alert system based on shared score for online social networks. In: Proceedings of the second international conference on information and communication technology for competitive strategies, ACM, p 108
Singh K, Bhola S, Lee W (2009) xbook: redesigning privacy control in social networking platforms. In: Proceedings of the 18th conference on USENIX security symposium, USENIX Association, Berkeley, CA, USA, SSYM’09, pp 249–266. http://dl.acm.org/citation.cfm?id=1855768.1855784
TextBlob (2015) Text Blob in Python documentation. https://pypi.python.org/pypi/textblob
Tuunainen VK, Pitkänen O, Hovi M (2009) Users’ awareness of privacy on online social networking sites-case facebook. In: Proceedings of Bled 2009, p 42
Wang P, Tan S (1997) Soft computing and fuzzy logic. Soft Comput 1(1):35–41. doi:10.1007/s005000050004
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
Zadeh LA (1983) The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets Syst 11(1–3):197–198. doi:10.1016/S0165-0114(83)80081-5
Zadeh LA (1988) Fuzzy logic. IEEE Comput 21(4):83–93. doi:10.1109/2.53
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Shanmuigapriya, T., Swamynathan, S. Reliability score inference and recommendation using fuzzy-based technique for social media applications. Soft Comput 22, 8289–8300 (2018). https://doi.org/10.1007/s00500-017-2774-5
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DOI: https://doi.org/10.1007/s00500-017-2774-5