Location sharing services have recently gained momentum over mobile online social networks (mOSNs), seeing the increasing popularity of GPS-capable mobile devices such as smart phones. Despite the convenience brought by location sharing, there comes severe privacy risks. Though many efforts have been made to protect user privacy during location sharing, many of them rely on the extensive deployment of trusted Cellular Towers (CTs) and some incur excessive time overhead. More importantly, little research so far can support complete privacy including location privacy, identity privacy and social relation privacy. We propose SAM, a new System Architecture for mOSNs, and P3S, a Privacy-Preserving Protocol based on SAM, to address the above issues for privacy-preserving location sharing over mOSNs. SAM and P3S differ from previous work in providing complete privacy for location sharing services over mOSNs. Theoretical analysis and extensive experimental results demonstrate the feasibility and efficiency of the proposed system and protocol.

Location-based services (LBSs) are useful for many applications in internet of things(IoT). However, LBSs has raised serious concerns about users' location privacy. In this paper, we propose a new location privacy attack in LBSs called hidden location inference attack, in which the adversary infers users' hidden locations based on the users' check-in histories. We discover three factors that influence individual check-in behaviors: geographic information, human mobility patterns and user preferences. We first separately evaluate the effects of each of these three factors on users' check-in behaviors. Next, we propose a novel algorithm that integrates the above heterogeneous factors and captures the probability of hidden location privacy leakage. Then, we design a novel privacy alert framework to warn users when their sharing behavior does not match their sharing rules. Finally, we use our experimental results to demonstrate the validity and practicality of the proposed strategy.

Predicting the routing paths between any given pair of Autonomous Systems (ASes) is very useful in network diagnosis, traffic engineering, and protocol analysis. Existing methods address this problem by resolving the best path with a snapshot of BGP (Border Gateway Protocol) routing tables. However, due to route deficiencies, routing policy changes, and other causes, the best path changes over time. Consequently, existing methods for path prediction fail to capture route dynamics. To predict AS-level paths in dynamic scenarios (e.g. network failures), we propose a per-neighbor path ranking model based on how long the paths have been used, and apply this routing model to extract each AS's route choice configurations for the paths observed in BGP data. With route choice configurations to multiple paths, we are able to predict the path in case of multiple network scenarios. We further build the model with strict policies to ensure our model's routing convergence; formally prove that it converges; and discuss the path prediction capturing routing dynamics by disabling links. By evaluating the consistency between our model's routing and the actually observed paths, we show that our model outperforms the state-of-the-art work [4].