Abstract
In this paper, we propose a framework for measuring the impact of data privacy techniques, in information theoretic and in data mining terms. The need for data privacy and anonymization is often hampered by the fact that the privacy functions alter the data in non-measurable amounts and details. We propose here to use Mutual Information over non-Euclidean spaces as a means of measuring this distortion. In addition, and following the same principle, we also propose to use Machine Learning techniques in order to quantify the impact of the data obfuscation in terms of further data mining goals.
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Miche, Y., Oliver, I., Holtmanns, S., Akusok, A., Lendasse, A., Björk, KM. (2016). On Mutual Information over Non-Euclidean Spaces, Data Mining and Data Privacy Levels. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-28373-9_32
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DOI: https://doi.org/10.1007/978-3-319-28373-9_32
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