Abstract
Mobile social networks are characterized by the sharing of user context information, such as the location of one’s mobile device. The location information on a social network enables its operator to offer resources for socialization, such as suggestions of new friends, products, and services, in relation to the user’s geographical area. While some users view such suggestions as a personal gain, others view them as an invasion of privacy. Since this location information is shared by the users on social networks, it can be accessed by the users’ friends and service providers, but also by malicious users. Unauthorized access can pose several risks regarding privacy and security. On the other hand, sharing a user’s location with a particular friend or group of friends while concealing this information from the service provider would guarantee the security and privacy of the user’s information. This paper presents a mobile social networking model with privacy guarantees concerning the sharing of its members’ locations. The model allows users to personalize their privacy by setting rules that determine to whom, when, and where their location information can be made available. The model provides three levels of privacy, personalized by the user, using techniques of anonymity and dissemination or setting the location to ensure the concealment of information before it is made available on the social network. A proof of concept for the proposed model, called RSMPrivacy, was developed for the Android platform. The performance tests showed that the delays generated by the use of RSMPrivacy were proportional to, and justifiable for, the privacy levels desired and chosen by the users. RSMPrivacy was evaluated by 50 users on the aspects of usability and evidence of the efficiency of the techniques in the proposed model.
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Rosa, T.A., Zorzo, S.D. (2015). Personalization of Privacy in Mobile Social Networks. In: Hammoudi, S., Maciaszek, L., Teniente, E., Camp, O., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2015. Lecture Notes in Business Information Processing, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-29133-8_27
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DOI: https://doi.org/10.1007/978-3-319-29133-8_27
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