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Rounding based continuous data discretization for statistical disclosure control
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab)ORCID-id: 0000-0002-2564-0683
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. Hamilton Institute, Maynooth University, Maynooth, Ireland. (Skövde Artificial Intelligence Lab)ORCID-id: 0000-0002-0368-8037
2019 (engelsk)Inngår i: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, s. 1-19Artikkel i tidsskrift (Fagfellevurdert) Epub ahead of print
Abstract [en]

“Rounding” can be understood as a way to coarsen continuous data. That is, low level and infrequent values are replaced by high-level and more frequent representative values. This concept is explored as a method for data privacy with techniques like rounding, microaggregation, and generalisation. This concept is explored as a method for data privacy in statistical disclosure control literature with perturbative techniques like rounding, microaggregation and non-perturbative methods like generalisation. Even though “rounding” is well known as a numerical data protection method, it has not been studied in depth or evaluated empirically to the best of our knowledge. This work is motivated by three objectives, (1) to study the alternative methods of obtaining the rounding values to represent a given continuous variable, (2) to empirically evaluate rounding as a data protection technique based on information loss (IL) and disclosure risk (DR), and (3) to analyse the impact of data rounding on machine learning based models. Here, in order to obtain the rounding values we consider discretization methods introduced in the unsupervised machine learning literature along with microaggregation and re-sampling based approaches. The results indicate that microaggregation based techniques are preferred over unsupervised discretization methods due to their fair trade-off between IL and DR. 

sted, utgiver, år, opplag, sider
Springer, 2019. s. 1-19
Emneord [en]
Micro data protection, Rounding for micro data, Unsupervised discretization, Discrete event simulation, Economic and social effects, Machine learning, Numerical methods, Volume measurement, Data protection techniques, Discretization method, Numerical data protection methods, Perturbative techniques, Statistical disclosure Control, Unsupervised machine learning, Data privacy
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
URN: urn:nbn:se:his:diva-17858DOI: 10.1007/s12652-019-01489-7Scopus ID: 2-s2.0-85074009425OAI: oai:DiVA.org:his-17858DiVA, id: diva2:1368632
Tilgjengelig fra: 2019-11-07 Laget: 2019-11-07 Sist oppdatert: 2020-01-29bibliografisk kontrollert

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