Opportunistic networking -forwarding messages in a disconnected mobile ad hoc network via any encountered nodes -offers a new mechanism for exploiting the mobile devices that many users now carry. Forwarding messages in such a network often involves the use of social-network routing-sending messages via nodes in the sender or recipient's friends list. Simple social-network routing, however, may broadcast these friends lists, which introduces privacy concerns. This paper presents a threat analysis of the privacy risks in social-network routing. We introduce two complementary methods for enhancing privacy in social-network routing by obfuscating the friends lists used to inform routing decisions. We evaluate these methods using three real-world datasets, and find that it is possible to obfuscate the friends lists without leading to a significant decrease in routing performance, as measured by delivery cost, delay and ratio. We quantify * Corresponding author. the increase in security provided by this obfuscation, with reference to classes of attack which are mitigated.

Abstract-Opportunistic networking -forwarding messages in a disconnected mobile ad hoc network via any encountered nodes -otters a new mechanism for exploiting the mobile devices that many users already carry. Forwarding messages in such a network often involves the use of social network routing-sending messages via nodes in the sender or recipient's social network. Simple social network routing, however, may broadcast these social networks, which introduces privacy concerns. This paper introduces two methods for enhancing privacy in social network routing by obfuscating the social network graphs used to inform routing decisions. We evaluate these methods using two real-world datasets, and find that it is possible to obfuscate the social network information without leading to a significant decrease in routing performance.

Large quantities of information are shared through online social networks, making them attractive sources of data for social network research. When studying the usage of online social networks, these data may not describe properly users' behaviours. For instance, the data collected often include content shared by the users only, or content accessible to the researchers, hence obfuscating a large amount of data that would help understanding users' behaviours and privacy concerns. Moreover, the data collection methods employed in experiments may also have an effect on data reliability when participants self-report inacurrate information or are observed while using a simulated application. Understanding the effects of these collection methods on data reliability is paramount for the study of social networks; for understanding user behaviour; for designing socially-aware applications and services; and for mining data collected from such social networks and applications. This chapter reviews previous research which has looked at social network data collection and user behaviour in these networks. We highlight shortcomings in the methods used in these studies, and introduce our own methodology and user study based on the Experience Sampling Method; we claim our methodology leads to the collection of more reliable data by capturing both those data which are shared and not shared. We conclude with suggestions for collecting and mining data from online social networks.

Opportunistic networking involves forwarding messages between proximate users, who may or may not know one another. This assumes that users are willing to forward messages to each other. This assumption may not hold if users are concerned about using the opportunistic network service. One such concern may be due to privacy; for instance, users' locations may be leaked.A privacy-concerned user may therefore disable their mobile device's opportunistic-networking features at various times, to preserve their privacy. This paper studies the impact of location privacy concerns on the performance of an opportunistic network. Using data from a real-world location-aware user study to develop a privacy model, we conduct trace-based simulations of various opportunistic routing protocols with two real-world traces. We find that users' location privacy preferences may potentially reduce the delivery performance of an opportunistic network to zero.

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