Efficient Privacy-Preserving Routing in OppNets With Probability Model and Discrete Optimization

Yang Gao, Jun Tao, Yifan Xu, Rujie Chen

Abstract: Opportunistic Networks (OppNets) can provide a low-cost and reliable way for the message forwarding in urban areas, especially in which traffic jams occur frequently. However, in terms of the OppNet based on the bicycle-sharing system (BSS), how to predict bicycle trips and improve routing performance still remains unsolved. Moreover, the exchange of auxiliary information among OppNet nodes (bike stations) will compromise the privacy of nodes/users. Thus we design the Two-Tier Probability Model (TTPM), including the InteR-day pattern and the IntrA-day pattern, to predict the trips accurately. Then the Discrete Optimization Differential Privacy (DODP) method is utilized to disturb the estimated InteR-day and IntrA-day probabilities, which will further protect the privacy of nodes and users. With TTPM and DODP, we propose an efficient privacy-preserving routing scheme for OppNet, which transforms the relay selection problem into the shortest path problem approximately. Extensive simulations show that the proposed routing scheme (TTPM) outperforms the benchmarks with the delivery ratio of more than 0.75 when the time-to-live is 5 days and the message generation rate is 6 pkts/hour. Compared with TTPM+Lap and TTPM+GRR, the proposed TTPM+DODP improves the delivery ratio by 30% and 3%, respectively.