Enhancing the Anonymity in Information Diffusion Based on Obfuscated Coded Data - Wang, Jin; Lu, Kejie; Wang, Jianping; Wu, Chuan; Gu, Naijie (IEEE transactions on network science and engineering, 2019)
Linear network coding (LNC) is a promising approach to facilitate anonymity in information diffusion because each packet is generated by linearly combining multiple incoming packets. Since the coefficients used in the linear combination would reveal the correlation between incoming and outgoing packets at a node, most existing studies on anonymous LNC design focus on encrypting these coefficients. Despite the importance of these studies, the correlation of coded content can still be analyzed and the potential of un-encrypted LNC has not been fully exploited. In this paper, we tackle these issues and we propose a novel ALNCode scheme that can enhance anonymity by generating outgoing packets that are correlated to incoming coded packets of multiple flows. With solid theoretical analysis, we first prove the probability that incoming coded packets from different flows are correlated. Then, we prove that, if such correlation exists, we can design deterministic LNC to obfuscate the correlation of packets. With the same condition, we also prove the probability that a randomly generated coded packet is correlated to coded packets in other flows. Besides the theoretical study, we conduct extensive numerical experiments to understand the impacts of various coding parameters and the performance of ALNCode in real scenarios.