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Recent research identifies a growing privacy problem that exists within online social networks (OSNs). Several studies have shown how easily strangers can extract personal data about users from the network. Other studies have shown that an extremely small percentage of OSN users change their permissive default privacy settings. Complementary systems have been proposed to provide privacy for OSN users but many of them seem to be costly in terms of simplicity or management overhead. Furthermore, several of these approaches seem to violate the social aspect of online social networks by reducing the problem to manual access management with cryptography. Under this setting, instead of freely sharing onepsilas data with its friends, a user will share data only with those friends possessing allowed cryptographic keys. While these systems will indeed provide additional privacy for user data, they will not address a more fundamental problem that exists within OSNs: the inability of the network to easily and automatically distinguish relationship quality between a user and its friends. This distinction may be critical in providing a simple, automatic privacy mechanism for OSNs. We propose a unique approach utilizing interactions between friends as the currency for data access. This model operates within the bounds of social networks while incurring minimum additional management overhead for the user. As a first step, we consider interaction intensity as a proxy for relationship quality for the purpose of making privacy decisions. Although we present our ideas from the privacy perspective we also discuss how our approach can be used to support the development of more secure social network based applications.
Computational Science and Engineering, 2009. CSE '09. International Conference on (Volume:4 )
Date of Conference: 29-31 Aug. 2009