Anonymity-based privacy preserving network data publication

Liu, Peng and Li, Yidong and Sang, Yingpeng and Zhang, Ji (2016) Anonymity-based privacy preserving network data publication. In: 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2016), 23-26 Aug 2016, Tianjin, China.

Abstract

Network trace data provide valuable information which contributes to model the network behaviors, defend network attacks and develop new protocols, so releasing the data of network trace is highly demanded by researchers and organizations to promote the development of the network technologies. However, due to the sensitive nature of network trace data, it is a potential risk for organizations to publish the original data which may expose their commercial confidentiality and the customers' privacy within their networks. Several methods to defend the network trace attacks such as statistical fingerprinting and injection have been proposed, unfortunately, they are not enough to protect the privacy because the correspondence between the source and destination IP addresses can also help the adversary to identify the target host. In this paper, we extract the inherent graph structure between the source and destination IP addresses in network trace data, and use k-anonymity to prevent the target host from being identified. Combined with other protection techniques, our method can also prevent the fingerprinting and injection attacks.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2016 IEEE. Permanent restricted access to published version due to publisher copyright policy.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 17 Feb 2017 06:45
Last Modified: 22 Jun 2017 02:27
Uncontrolled Keywords: IP networks; measurement; bipartite graph; cryptography; data privacy; data models; organizations
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
Identification Number or DOI: 10.1109/TrustCom.2016.0144
URI: http://eprints.usq.edu.au/id/eprint/30676

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