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Thought Off-line Sanitization Methods for Bank Transactions

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Information Management and Big Data (SIMBig 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 898))

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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.

Supported by the research fund projects of the Vicerrectorate of the Universidad del Pacífico PY-ESP-0210013216.

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Notes

  1. 1.

    General Data Protection Regulation: www.eugdpr.org.

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Authors and Affiliations

  1. Universidad del Pacífico, Av. Salaverry 2020, Lima, Peru

    Isaias Hoyos & Miguel Nunez-del-Prado

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  1. Isaias Hoyos
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  2. Miguel Nunez-del-Prado
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Correspondence to Miguel Nunez-del-Prado .

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Editors and Affiliations

  1. Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA

    Juan Antonio Lossio-Ventura

  2. Fondazione Bruno Kessler, Trento, Italy

    Denisse Muñante

  3. Facultad de Ingeniería, University of the Pacific, Jesús María, Lima, Peru

    Hugo Alatrista-Salas

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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

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  • DOI: https://doi.org/10.1007/978-3-030-11680-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11679-8

  • Online ISBN: 978-3-030-11680-4

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