Enhancing the Anonymity in Information Diffusion Based on Obfuscated Coded Data

作     者：Wang, Jin Lu, Kejie Wang, Jianping Wu, Chuan Gu, Naijie 

作者机构：Soochow Univ Dept Comp Sci & Technol Suzhou 215000 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Univ Puerto Rico Mayaguez Dept Comp Sci & Engn Mayaguez PR 00682 USA Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Univ Sci & Technol China Dept Comp Sci Hefei 230022 Anhui Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING》 (IEEE Trans. Netw. Sci. Eng.)

年 卷 期：2019年第6卷第4期

页      面：968-982页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0701[理学-数学] 

基　　金：National Natural Science Foundation of China [61672370, 61502328] General Research Fund from Hong Kong Research Grant Council Shanghai Oriental Scholar Program Natural Science Foundation of the Higher Education Institutions of Jiangsu Province [16KJB520040] Science Technology and Innovation Committee of Shenzhen Municipality [JCYJ20170818095109386] NSFC-Guangdong Joint Fund [U1501254] National Science Foundation [CNS-1730325] 

主　　题：Anonymity information diffusion network coding secure linear network coding deterministic linear network coding random linear network coding traffic analysis 

摘      要：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.