Privacy: thinking data is anonymous
Removing names does not anonymise, and the model itself leaks its training data. All four claims are measured.
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Sources
- Sweeney, L. 2002 · k-Anonymity: A Model for Protecting Privacy · Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5)
- Shokri, R. et al. 2017 · Membership Inference Attacks Against Machine Learning Models · IEEE S&P 2017
- Dwork, C. & Roth, A. 2014 · The Algorithmic Foundations of Differential Privacy · Foundations and Trends in Theoretical Computer Science, 9(3-4)
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