# Supervised Machine Learning with Plausible Deniability

arXiv: https://arxiv.org/abs/2106.04267 · HTML: https://mlsanity.com/paper/2106.04267/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Privacy Preserving Machine Learning: Threats and Solutions](https://mlsanity.com/paper/1804.11238/) (2018)
- [When the signal is in the noise: Exploiting Diffix's Sticky Noise](https://mlsanity.com/paper/1804.06752/) (2018)
- [Machine learning and genomics: precision medicine vs. patient privacy](https://mlsanity.com/paper/1802.10568/) (2018)

## Built on by (most cited first, 1 of 1)

- [Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions](https://mlsanity.com/paper/2311.17453/) (2023)

## Abstract (arXiv)

We study the question of how well machine learning (ML) models trained on a certain data set provide privacy for the training data, or equivalently, whether it is possible to reverse-engineer the training data from a given ML model. While this is easy to answer negatively in the most general case, it is interesting to note that the protection extends over non-recoverability towards plausible deniability: Given an ML model f, we show that one can take a set of purely random training data, and from this define a suitable ``learning rule'' that will produce a ML model that is exactly f. Thus, any speculation about which data has been used to train f is deniable upon the claim that any other data could have led to the same results. We corroborate our theoretical finding with practical examples, and open source implementations of how to find the learning rules for a chosen set of raining data.