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Privacy-Preserving Public Release of Datasets for Support Vector Machine Classification
[article]
2019
arXiv
pre-print
We consider the problem of publicly releasing a dataset for support vector machine classification while not infringing on the privacy of data subjects (i.e., individuals whose private information is stored in the dataset). The dataset is systematically obfuscated using an additive noise for privacy protection. Motivated by the Cramer-Rao bound, inverse of the trace of the Fisher information matrix is used as a measure of the privacy. Conditions are established for ensuring that the classifier
arXiv:1912.12576v1
fatcat:pftsj6t5cbcdleqbkjb4jsvqzi