Abstract
Effective data anonymisation is the key to unleashing the full potential of big data analytics while preserving privacy. An organization needs to be able to share and consolidate the data it collects across its departments and in its network of collaborating organizations. Some of the data collected and the cross-references made in its aggregation is private. Effective data anonymisation attempts to maintain the confidentiality and privacy of the data while maintaining its utility for the purpose of analytics. Preventing re-identification is also of particular importance. The main purpose of this paper is to provide a definition of an original data anonymisation paradigm in order to render the re-identification of related users impossible. Here, we consider the case of a NetFlow Log. The solution includes a privacy risk analysis process to classify the data based on its privacy level. We use a dynamic K-anonymity paradigm while taking into consideration the privacy risk assessment output. Finally, we empirically evaluate the performance and data partition of the proposed solution.
This work has been supported, in part, by the IDOLE ANR project in France as well as the National Research Foundation in Singapore including the Prime Minister’s Office, Singapore under its Corporate Laboratory@University Scheme, the National University of Singapore and Singapore Telecommunications Ltd.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalisation and suppression. Technical report, SRI International (1998)
Sweeney, L.: Computational disclosure control for medical microdata: the Datafly system. In: Record Linkage Techniques 1997: Proceedings of an International Workshop and Exposition (1997)
Slagell, A.J., Lakkaraju, K., Luo, K.: FLAIM: a multi-level anonymisation framework for computer and network logs. In: LISA, vol. 6 (2006)
Foukarakis, M., et al.: Flexible and high-performance anonymisation of NetFlow records using anontool. In: Third International Conference on Security and Privacy in Communications Networks and the Workshops, SecureComm 2007. IEEE (2007)
Farah, T., Trajković, L.: Anonym: a tool for anonymisation of the Internet traffic. In: 2013 IEEE International Conference on Cybernetics (CYBCONF). IEEE (2013)
Rajendran, K., Jayabalan, M., Rana, M.E.: A study on k-anonymity, l-diversity, and t-closeness techniques focusing medical data. IJCSNS Int. J. Comput. Sci. Netw. Secur. 17(12), 172 (2017)
Hussien, A.A., Hamza, N., Hefny, H.A.: Attacks on anonymisation-based privacy-preserving: a survey for data mining and data publishing. J. Inf. Secur. 4(2), 101–112 (2013). https://doi.org/10.4236/jis.2013.42012
Jain, P., Gyanchandani, M., Khare, N.: Big data privacy: a technological perspective and review. J. Big Data 3(1), 25 (2016). https://doi.org/10.1186/s40537-016-0059-y
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(5), 571–588 (2002). https://doi.org/10.1142/s021848850200165x
Li, N., Li, T., Venkatasubramanian, S.: T-closeness: privacy beyond k-anonymity and l-diversity. In: ICDE 2007 IEEE 23rd International Conference on Data Engineering (2007). https://doi.org/10.1109/icde.2007.367856.
Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: L-diversity: privacy beyond k-anonymity. In: 22nd IEEE International Conference on Data Engineering (ICDE 2006), Atlanta, Georgia, April 2006
Bild, R., Kuhn, K.A., Prasser, F.: Safepub: a truthful data anonymisation algorithm with strong privacy guarantees. Proc. Priv. Enhancing Technol. 1, 67–87 (2018)
Slagell, A., Wang, J., Yurcik, W.: Network log anonymisation: application of crypto-pan to cisco netflows (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Aloui, A., Msahli, M., Abdessalem, T., Mesnager, S., Bressan, S. (2020). Privacy as a Service: Anonymisation of NetFlow Traces. In: Chao, KM., Jiang, L., Hussain, O., Ma, SP., Fei, X. (eds) Advances in E-Business Engineering for Ubiquitous Computing. ICEBE 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-030-34986-8_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-34986-8_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34985-1
Online ISBN: 978-3-030-34986-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)