Abstract
In the digital era, people generate a lot of digital traces ranging from posts on social networks, call detail records and credit or debit banks transactions among others. These data could help society to understand different urban phenomena such as what citizens are talking about, how they commute or what are their spending behaviors. Therefore, the use of such data trigger privacy issues. In the present effort, we study four different Statistical Disclosure Control filters to sanitize off-line credit or debit bank transactions. Consequently, we analyze Noise Addition, Microaggregation, Rank Swapping and Differential Privacy filters concerning Disclosure Risk, Information Loss, and utility. We observed that Microaggregation and Different Privacy perform very well for minimizing Disclosure Risk while providing a good utility for statistics of spending amounts per industry type.
Keywords
Supported by the research fund projects of the Vicerrectorate of the Universidad del Pacífico PY-ESP-0210013216.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
General Data Protection Regulation: www.eugdpr.org.
References
Brand, R.: Tests of the applicability of sullivan algorithm to synthetic data and real business data in official statistics. European Project IST-2000-25069 CASC (2002)
DeWaal, A., Willenborg, L.: Global recodings and local suppressions in microdata sets. In: Proceedings of Statistics Canada Symposium, vol. 95, pp. 121–132 (1995)
Domingo-Ferrer, J., Mateo-Sanz, J.M.: Practical data-oriented microaggregation for statistical disclosure control. IEEE Trans. Knowl. Data Eng. 14(1), 189–201 (2002)
Domingo-Ferrer, J., Torra, V.: Disclosure risk assessment in statistical data protection. J. Comput. Appl. Math. 164, 285–293 (2004)
Domingo-Ferrer, J., Sebé, F., Castellà-Roca, J.: On the security of noise addition for privacy in statistical databases. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 149–161. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25955-8_12
Domingo-Ferrer, J., Sebé, F., Solanas, A.: A polynomial-time approximation to optimal multivariate microaggregation. Comput. Math. Appl. 55(4), 714–732 (2008)
Domingo-Ferrer, J., Torra, V.: A quantitative comparison of disclosure control methods for microdata. In: Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, pp. 111–134 (2001)
Hundepool, A., et al.: Statistical Disclosure Control. Wiley, Hoboken (2012)
Kim, J.: A method for limiting disclosure in microdata based on random noise and transformation (2002)
Laszlo, M., Mukherjee, S.: Minimum spanning tree partitioning algorithm for microaggregation. IEEE Trans. Knowl. Data Eng. 17(7), 902–911 (2005)
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)
Leoni, D.: Non-interactive differential privacy: a survey. In: Proceedings of the First International Workshop on Open Data, WOD 2012. ACM, New York, pp. 40–52 (2012)
Li, N., Lyu, M., Su, D., Yang, W.: Differential privacy: from theory to practice. Synth. Lect. Inf. Secur. Priv. Trust. 8, 1–138 (2016)
Muralidhar, K., Sarathy, R.: Data shufflinga new masking approach for numerical data. Manag. Sci. 52(5), 658–670 (2006)
Nin, J., Herranz, J., Torra, V.: Rethinking rank swapping to decrease disclosure risk. Data Knowl. Eng. 64(1), 346–364 (2008)
Oganian, A.: Multiplicative noise protocols. In: Domingo-Ferrer, J., Magkos, E. (eds.) PSD 2010. LNCS, vol. 6344, pp. 107–117. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15838-4_10
Oganian, A.: Multiplicative noise for masking numerical microdata with constraints. SORT 35, 99–112 (2011)
Reiss, S.P.: Practical data-swapping: the first steps. ACM Trans. Database Syst. (TODS) 9(1), 20–37 (1984)
Reiss, S.P., Post, M.J., Dalenius, T.: Non-reversible privacy transformations. In: Proceedings of the 1st ACM SIGACT-SIGMOD Symposium on Principles of Database Systems. ACM, pp. 139–146 (1982)
Rodríguez, D.M., Nin, J., Nuñez-del Prado, M.: Towards the adaptation of SDC methods to stream mining. Comput. Secur. 70, 702–722 (2017)
Sebé, F., Domingo-Ferrer, J., Mateo-Sanz, J.M., Torra, V.: Post-Masking optimization of the tradeoff between information loss and disclosure risk in masked microdata sets. In: Domingo-Ferrer, J. (ed.) Inference Control in Statistical Databases. LNCS, vol. 2316, pp. 163–171. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47804-3_13
Sullivan, G.R.: The use of added error to avoid disclosure in microdata releases. Iowa State University, Unpublished Ph.D. dissertation (1989)
Templ, M.: Statistical Disclosure Control for Microdata: Methods and Applications in R. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-50272-4
Templ, M., Meindl, B.: Robust statistics meets SDC: new disclosure risk measures for continuous microdata masking. In: Domingo-Ferrer, J., Saygın, Y. (eds.) PSD 2008. LNCS, vol. 5262, pp. 177–189. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87471-3_15
Willenborg, L., De Waal, T.: Statistical Disclosure Control in Practice, vol. 111. Springer, New York (1996). https://doi.org/10.1007/978-1-4612-4028-0
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hoyos, I., Nunez-del-Prado, M. (2019). Thought Off-line Sanitization Methods for Bank Transactions. In: Lossio-Ventura, J., Muñante, D., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2018. Communications in Computer and Information Science, vol 898. Springer, Cham. https://doi.org/10.1007/978-3-030-11680-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-11680-4_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11679-8
Online ISBN: 978-3-030-11680-4
eBook Packages: Computer ScienceComputer Science (R0)