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

arXiv: https://arxiv.org/abs/1708.08022 · HTML: https://mlsanity.com/paper/1708.08022/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2017.

## Built on by (most cited first, 4 of 4)

- [Survey: Leakage and Privacy at Inference Time](https://mlsanity.com/paper/2107.01614/) (2021)
- [Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning](https://mlsanity.com/paper/2207.12831/) (2022)
- [Inference Attacks: A Taxonomy, Survey, and Promising Directions](https://mlsanity.com/paper/2406.02027/) (2024)
- [DP-CDA: An Algorithm for Enhanced Privacy Preservation in Dataset Synthesis Through Randomized Mixing](https://mlsanity.com/paper/2411.16121/) (2024)

## Abstract (arXiv)

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.