Scalable and Privacy Preserving Routing in Mobile Social Networks

Abstract

Mobility-assisted opportunistic routing in mobile social network is an interesting research topic with real world applications. It allows users to exchange large chunks of data without relying on stationary network infrastructures. This paper proposes a scalability and privacy preserving routing algorithm. The core of a multi-hop routing scheme is the prediction of the next relay router, which ideally is the next hop on the shortest path to the destination of the message. The proposed routing algorithm contains a machine learning based prediction model that is trained on a trace of nodes connection events collected during a warm up period in a network where the mobility is assumed to exhibit a certain degree of regularity as in general human social networks. The algorithm is scalable since the prediction model, i.e. the control plane of the algorithm, only needs to maintain and broadcast the trained model that implicitly encodes the possible mobility locations and patterns within the mobility area, which is a constant to the number of nodes in the network. The algorithm is privacy preserving because that it does not require the nodes to disclose to other nodes any explicit information about its previous mobility or its personal preferences. Privacy preserving is achieved by using distributed node representations that jointly encode the mobility pattern of the node together with the prediction model, instead of using explicit statistical information to represent previous connectivity patterns as in prior work. This on one hand make attacker hard to find out any exact information about any particular node, and on the other hand enables the routing algorithm to extrapolate to the prediction of nodes of unseen mobility pattern, which is difficult if hand designed statistical mobility information is used. To perform large scale experiments, we collect very large synthetic mobility trace from university course registration information, this trace contain a total of about 35,000 nodes and 300 million message forwarding links. Experiments are conduct to examine several prediction accuracies of our algorithm.