Abstract
Data growth is exponential and nearly immeasurable. We used to talk about megabytes when we spoke about data, but now we talk about petabytes with BigData. This data growth makes sensitive data and identifiers increasingly exposed. To address this issue, there is anonymization data, which attempts to “mask” the data so that it is nearly difficult to identify and correlate persons with them; yet, the data remains usable for statistical reasons, among other things. To avoid falling behind in these technical difficulties, many businesses employ free, open-source software. However, this software is not always secure or meets the user’s expectations. The goal of OSSpal is to normalize these concerns.
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Acknowledgements
“This work is funded by National Funds through the FCT—Foundation for Science and Technology, IP, within the scope of the project Ref UIDB/05583/2020. Furthermore, we would like to thank the Research Centre in Digital Services (CISeD), the Polytechnic of Viseu, for their support.”
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Monteiro, S. et al. (2024). Data Anonymization: Techniques and Models. In: Reis, J.L., Del Rio Araujo, M., Reis, L.P., dos Santos, J.P.M. (eds) Marketing and Smart Technologies. ICMarkTech 2022. Smart Innovation, Systems and Technologies, vol 344. Springer, Singapore. https://doi.org/10.1007/978-981-99-0333-7_6
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DOI: https://doi.org/10.1007/978-981-99-0333-7_6
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