A Privacy-reserved Approach for Message Forwarding in Opportunistic Networks

摘要

Opportunistic Network (OppNet) is an emerging communication paradigm, by which nodes inside forward messages through personal contact opportunities. Recently, numerous studies have focused on predicting nodes meeting to promote routing efficiency and reduce transmission delay. However, individual privacy would likely be revealed to strangers or attackers during the execution of prediction. In this paper, we construct a privacy-reserved network framework for message forwarding to guarantee both efficient communication and individual privacy in OppNets, including a security-based prediction method and an attribute-based cryptosystem. Simulation results demonstrate that, our algorithm outperforms TRSS on average delivery ratio, generally by 15% for dropping probability and data tempered probability.