A research on hierarchical trackback technique for individual big data

Hong Zhang, Bing Guo, Yuncheng Shen, Xuliang Duan, Xiangqian Dong, Yan Shen
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Abstract

In order to solve the privacy protection problem of individual big data, this paper proposes a hierarchical data trackback technique (HDTT). This technique can realise the data trackback through inter-domain and intra-domain path reconstruction without increasing the core network storage load. The main method is as follows: record the AS domain involved by data packets and IP address information with GBF data structure by use of idle part of packet header, determine the AS domain first with GBFAS data during the path reconstruction, and then determine the intra-domain router with GBFIP data to complete the data trackback. Finally, through the verification of Data Collect Treasure platform by project group, the contact ratio between inter-domain and intra-domain paths is up to over 98% and 92%, respectively, so HDTT technique can accurately reconstruct the data flow path, realise the data trackback and achieve the privacy protection of individual big data.
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个体大数据分层回溯技术研究
为了解决个体大数据的隐私保护问题,本文提出了一种分层数据追溯技术(HDTT)。该技术可以在不增加核心网存储负荷的情况下,通过域间和域内路径重构实现数据溯源。主要方法是:利用包头的空闲部分,用GBF数据结构记录数据包所涉及的as域和IP地址信息,在路径重构时先用GBFIP数据确定as域,再用GBFIP数据确定域内路由器,完成数据溯源。最后,通过项目组对数据采集宝平台的验证,域间路径和域内路径的接触率分别达到98%以上和92%以上,HDTT技术可以准确重构数据流路径,实现数据追溯,实现个体大数据的隐私保护。
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