Abstract
This paper proposes a method to anonymize network trace data by utilizing a novel perturbation technique that has strong privacy guarantee and at the same time preserves data utility. The resulting dataset can be used for security analysis, retaining the utility of the original dataset, without revealing sensitive information. Our method utilizes a condensation based approach with strong privacy guarantees, suited for cloud environments. Experiments show that the method performs better than existing anonymization techniques in terms of privacy-utility trade off, and it surpasses existing techniques in attack prediction accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pang, R., Allman, M., Paxson, V., Lee, J.: The devil and packet trace anonymization. ACM SIGCOMM Comput. Commun. Rev. 36, 29–38 (2006)
RIPE (RIPE Network Coordination Centre). https://www.ripe.net/
University of Oregon Route Views Project. http://www.routeviews.org/
Corporative Association for Internet Data Analysis (CAIDA). https://www.caida.org/tools/taxonomy/anonymization.xml
Xu, J., Fan, J., Ammar, M., Moon, S.B.: On the design and performance of prefix-preserving IP traffic trace anonymization. In: Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement, pp. 263–266. ACM (2001)
Burkhart, M., Schatzmann, D., Trammell, B., Boschi, E., Plattner, B.: The role of network trace anonymization under attack. ACM SIGCOMM Comput. Commun. Rev. 40, 5–11 (2010)
Brekne, T., Årnes, A., Øslebø, A.: Anonymization of IP traffic monitoring data: attacks on two prefix-preserving anonymization schemes and some proposed remedies. In: Danezis, G., Martin, D. (eds.) PET 2005. LNCS, vol. 3856, pp. 179–196. Springer, Heidelberg (2006)
Chaum, D.L.: Untraceable electronic mail, return addresses, and digital pseudonyms. Commun. ACM 24, 84–90 (1981)
Raymond, J.-F.: Traffic analysis: protocols, attacks, design issues, and open problems. In: Federrath, H. (ed.) Designing Privacy Enhancing Technologies. LNCS, vol. 2009, pp. 10–29. Springer, Heidelberg (2001)
Slagell, A.J., Li, Y., Luo, K.: Sharing network logs for computer forensics: a new tool for the anonymization of netflow records. In: Workshop of the 1st International Conference on Security and Privacy for Emerging Areas in Communication Networks, pp. 37–42. IEEE (2005)
Gamer, T., Mayer, C., Schöller, M.: PktAnon–A Generic Framework for Profile-based Traffic Anonymization. PIK-Praxis der Informationsverarbeitung und Kommunikation 31, 76–81 (2008)
Qardaji, W., Li, N.: Anonymizing network traces with temporal pseudonym consistency. In: 2012 32nd International Conference on Distributed Computing Systems Workshops, pp. 622–633. IEEE (2012)
Peuhkuri, M.: A method to compress and anonymize packet traces. In: Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement, pp. 257–261. ACM (2001)
Fan, J., Xu, J., Ammar, M.H., Moon, S.B.: Prefix-preserving IP address anonymization: measurement-based security evaluation and a new cryptography-based scheme. Comput. Netw. 46, 253–272 (2004)
Riboni, D., Villani, A., Vitali, D., Bettini, C., Mancini, L.V.: Obfuscation of sensitive data in network flows. In: Proceedings of IEEE INFOCOM, pp. 2372–2380. IEEE (2012)
Slagell, A., Yurcik, W.: Sharing computer network logs for security and privacy: a motivation for new methodologies of anonymization. In: Workshop of the 1st International Conference on Security and Privacy for Emerging Areas in Communication Networks, pp. 80–89. IEEE (2005)
Slagell, A., Wang, J., Yurcik, W.: Network log anonymization: application of crypto-pan to cisco netflows. In: Proceedings of the Workshop on Secure Knowledge Management (2004)
Shebaro, B., Crandall, J.R.: Privacy-preserving network flow recording. Digital Invest. 8, S90–S100 (2011)
Koukis, D., Antonatos, S., Antoniades, D., Markatos, E.P., Trimintzios, P.: A generic anonymization framework for network traffic. In: IEEE International Conference on Communications, pp. 2302–2309. IEEE (2006)
King, J., Lakkaraju, K., Slagell, A.: A taxonomy and adversarial model for attacks against network log anonymization. In: Proceedings of the ACM symposium on Applied Computing, pp. 1286–1293. ACM (2009)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10, 557–570 (2002)
Aggarwal, C.C., Yu, P.S.: A condensation approach to privacy preserving data mining. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 183–199. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24741-8_12
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, pp. 281–297 (1967)
Miller, D.: Softflowd: A Flow-Based Network Traffic Analyser (2013). Mindrot.org
Agrawal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the Twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pp. 247–255. ACM (2001)
Acknowledgement
This work is partially supported by MITRE-USM FFRDC under grant 11183.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Aleroud, A., Chen, Z., Karabatis, G. (2016). Network Trace Anonymization Using a Prefix-Preserving Condensation-Based Technique (Short paper). In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2016 Conferences. OTM 2016. Lecture Notes in Computer Science(), vol 10033. Springer, Cham. https://doi.org/10.1007/978-3-319-48472-3_59
Download citation
DOI: https://doi.org/10.1007/978-3-319-48472-3_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48471-6
Online ISBN: 978-3-319-48472-3
eBook Packages: Computer ScienceComputer Science (R0)