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Towards a Data Privacy-Predictive Performance Trade-off. (arXiv:2201.05226v1 [cs.LG])
Jan. 17, 2022, 2:10 a.m. | Tânia Carvalho, Nuno Moniz, Pedro Faria, Luís Antunes
cs.LG updates on arXiv.org arxiv.org
Machine learning is increasingly used in the most diverse applications and
domains, whether in healthcare, to predict pathologies, or in the financial
sector to detect fraud. One of the linchpins for efficiency and accuracy in
machine learning is data utility. However, when it contains personal
information, full access may be restricted due to laws and regulations aiming
to protect individuals' privacy. Therefore, data owners must ensure that any
data shared guarantees such privacy. Removal or transformation of private
information (de-identification) …
arxiv data data privacy performance predictive privacy trade
More from arxiv.org / cs.LG updates on arXiv.org
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