Abstract
Online Social Networks (OSNs) are actively being used by a large fraction of people. People extensively share a wealth of their information online. This content if retrieved, stored, processed and spread beyond scope without user’s consent may result in a privacy breach. Adopting a coarse grained privacy mechanism such as sharing information to a group of “close friends” or the strong ties of the network is one of the solutions to minimize the risk of unwanted disclosure but this does not fully contribute in the process of protecting privacy. There is a high probability for an unwanted information disclosure even if the information is shared only with the strong ties. In most of the privacy literature while building trust the online sharing behavior is never looked into consideration. Hence, in this paper we propose and implement a privacy preserving model where such unwanted and unintentional private information disclosures could be minimized by further refining the trusted community of strong ties with respect to their privacy quotient.
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Srivastava, A., Krishnakumar, K.P., Geethakumari, G. (2015). Quantifying Direct Trust for Private Information Sharing in an Online Social Network. In: Buyya, R., Thampi, S. (eds) Intelligent Distributed Computing. Advances in Intelligent Systems and Computing, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-11227-5_3
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DOI: https://doi.org/10.1007/978-3-319-11227-5_3
Publisher Name: Springer, Cham
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