Title: A research on hierarchical trackback technique for individual big data

Authors: Hong Zhang; Bing Guo; Yun-Cheng Shen; Xu-Liang Duan; Xiang-Qian Dong; Yan Shen

Addresses: Department of Computer Science, Sichuan University, Chengdu, 610065, China; College of Information Science and Engineering, Chengdu University, Chengdu, 610106, China ' Department of Computer Science, Sichuan University, Chengdu, 610065, China ' Department of Computer Science, Sichuan University, Chengdu, 610065, China ' Department of Computer Science, Sichuan University, Chengdu, 610065, China ' Department of Computer Science, Sichuan University, Chengdu, 610065, China ' Department of Control Engineering, Chengdu University of Information Technology, Chengdu, 610225, China

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.

Keywords: individual big data; GBF data structure; IP trackback.

DOI: 10.1504/IJES.2020.107037

International Journal of Embedded Systems, 2020 Vol.12 No.3, pp.294 - 304

Received: 08 Jun 2018
Accepted: 18 Oct 2018
Published online: 01 May 2020 *

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