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On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches

Úlfar Erlingsson
Ian Goodfellow
Nicolas Papernot
Ilya Mironov
Kunal Talwar
Li Zhang
Proceedings of 30th IEEE Computer Security Foundations Symposium (CSF) (2017), pp. 1-6

Abstract

The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy. However, older ideas about privacy may well remain valid and useful. This note reviews two recent works on privacy in the light of the wisdom of some of the early literature, in particular the principles distilled by Saltzer and Schroeder in the 1970s.