On the Application of SDC Stream Methods to Card Payments Analytics

  • Miguel Nuñez-del-Prado
  • Jordi NinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11144)


Banks and financial services have to constantly innovate their online payment services to avoid large digital companies take the control of online card transactions, relegating traditional banks to simple payments carriers. Apart from creating new payment methods (e.g. contact-less cards, mobile wallets, etc.), banks offers new services based on historical payments data to endow traditional payments methods with new services and functionalities. In this latter case, it is where privacy preserving techniques play a fundamental role ensuring personal data is managed full-filling all the applicable laws and regulations. In this paper, we introduce some ideas about how SDC stream anonymization methods could be used to mask payments data streams. Besides, we also provide some experimental results over a real card payments database.


Statistical Disclosure Control General Data Protection Regulation (GDPR) Payment Service Directive (PSD2) Stream mining 


  1. 1.
    Domingo-Ferrer, J., Torra, V.: Disclosure control methods and information loss for microdata. In: Confidentiality, Disclosure, and Data Access: Theory and Practical Applications for Statistical Agencies, pp. 91–110. Elsevier Science (2001)Google Scholar
  2. 2.
    Domingo-Ferrer, J., Torra, V.: Disclosure risk assessment in statistical data protection. J. Comput. Appl. Math. 164, 285–293 (2003)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Domingo-Ferrer, J., Sebé, F., Solanas, A.: A polynomial-time approximation to optimal multivariate microaggregation. Comput. Math. Appl. 55(4), 714–732 (2008)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). Scholar
  5. 5.
    Hundepool, A., et al.: Statistical Disclosure Control. Wiley, New York (2012)CrossRefGoogle Scholar
  6. 6.
    Leo, Y., Karsai, M., Sarraute, C., Fleury, E.: Correlations of consumption patterns in social-economic networks. In: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 493–500. IEEE (2016)Google Scholar
  7. 7.
    Leoni, D.: Non-interactive differential privacy: a survey. In: Proceedings of the First International Workshop on Open Data, WOD 2012, pp. 40–52. ACM, New York (2012)Google Scholar
  8. 8.
    Li, N., Lyu, M., Su, D., Yang, W.: Differential Privacy: From Theory to Practice. Synthesis Lectures on Information Security, Privacy, vol. 8, pp. 1–138 (2016)CrossRefGoogle Scholar
  9. 9.
    Martínez-Rodríguez, D., Nin, J., Nuñez-del-Prado, M.: Towards the adaptation of SDC methods to stream mining. Comput. Secur. 70, 702–722 (2017)CrossRefGoogle Scholar
  10. 10.
    Mateo-Sanz, J.M., Domingo-Ferrer, J., Sebé, F.: Probabilistic information loss measures in confidentiality protection of continuous microdata. Data Min. Knowl. Discov. 11(2), 181–193 (2005)MathSciNetCrossRefGoogle Scholar
  11. 11.
  12. 12.
    Information Commissioner’s Office Guide to the General Data Protection Regulation (GDPR).
  13. 13.
    Navarro-Arribas, G., Torra, V.: Rank swapping for stream data. In: Torra, V., Narukawa, Y., Endo, Y. (eds.) MDAI 2014. LNCS (LNAI), vol. 8825, pp. 217–226. Springer, Cham (2014). Scholar
  14. 14.
    Nin, J., Herranz, J., Torra, V.: Rethinking rank swapping to decrease disclosure risk. Data Knowl. Eng. 64(1), 346–364 (2008)CrossRefGoogle Scholar
  15. 15.
    Soria-Comas, J., Domingo-Ferrer, J., Sánchez, D., Martínez, S.: Enhancing data utility in differential privacy via microaggregation-based k-anonymity. Very Large Data Base J. 23(5), 771–794 (2014)CrossRefGoogle Scholar
  16. 16.
    Templ, M., Meindl, B., Kowarik, A.: Introduction to statistical disclosure control (SDC).

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Universidad del PacíficoLimaPeru
  2. 2.BBVA Data & AnalyticsBarcelonaSpain

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