Advertisement

Privacy as a Service: Anonymisation of NetFlow Traces

  • Ashref AlouiEmail author
  • Mounira Msahli
  • Talel Abdessalem
  • Sihem Mesnager
  • Stéphane Bressan
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 41)

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.

Keywords

Privacy Anonymisation Risk analysis NetFlow 

References

  1. 1.
    Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalisation and suppression. Technical report, SRI International (1998)Google Scholar
  2. 2.
    Sweeney, L.: Computational disclosure control for medical microdata: the Datafly system. In: Record Linkage Techniques 1997: Proceedings of an International Workshop and Exposition (1997)Google Scholar
  3. 3.
    Slagell, A.J., Lakkaraju, K., Luo, K.: FLAIM: a multi-level anonymisation framework for computer and network logs. In: LISA, vol. 6 (2006)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Farah, T., Trajković, L.: Anonym: a tool for anonymisation of the Internet traffic. In: 2013 IEEE International Conference on Cybernetics (CYBCONF). IEEE (2013)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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.2013.42012CrossRefGoogle Scholar
  8. 8.
    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-yCrossRefGoogle Scholar
  9. 9.
    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/s021848850200165xMathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    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.
  11. 11.
    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 2006Google Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Slagell, A., Wang, J., Yurcik, W.: Network log anonymisation: application of crypto-pan to cisco netflows (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ashref Aloui
    • 1
    Email author
  • Mounira Msahli
    • 2
  • Talel Abdessalem
    • 2
  • Sihem Mesnager
    • 1
  • Stéphane Bressan
    • 3
  1. 1.Université Paris 8Saint-DenisFrance
  2. 2.Télécom ParisTechInstitut Mines-TelecomParisFrance
  3. 3.National University of SingaporeSingaporeSingapore

Personalised recommendations