Iain Parris

Privacy-enhanced social-network routing

An opportunistic network of mobile nodes can be created when mobile devices work together to create an ad hoc store-and-forward architecture, with messages forwarded via intermediary encountered nodes. Social-network routing has been proposed to route messages in such networks: messages are sent via nodes in the senders or recipients friends list. Simple social-network routing, however, may broadcast these friends lists, which introduces privacy concerns. This paper studies mechanisms for enhancing privacy while using social-network routing. We first present a threat analysis of the privacy risks in social-network routing, and then 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 the increase in security provided by this obfuscation, with reference to the classes of attack which are mitigated.

Reliable online social network data collection

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 to understand 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 inaccurate 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 that 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.

Privacy-enhanced social network routing in opportunistic networks

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 already carry. Forwarding messages in such a network often involves the use of social network routing— sending messages via nodes in the sender or recipients 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.

Facebook or Fakebook? The effects of simulated mobile applications on simulated mobile networks

The credibility of mobile ad hoc network simulations depends on accurate characterisations of user behaviour, e.g., mobility and application usage. If simulated nodes communicate at different rates to real nodes, or move in an unrealistic fashion, this may have a large impact on the network protocols being simulated and tested. Many future mobile network protocols, however, may also depend on future mobile applications. Different applications may be used at different rates or in different manners. But how can we determine realistic user behaviour for such applications that do not yet exist? One common solution is again simulation, but this time simulation of these future applications. This paper examines differences in user behaviour between a real and simulated mobile social networking application through a user study (n=80). We show that there are distinct differences in privacy behaviour between the real and simulated groups. We then simulate a mobile opportunistic network application using two real-world traces to demonstrate the impact of using real and simulated applications. We find large differences between using real and synthetic models of privacy behaviour, but smaller differences between models derived from the real and simulated applications.