# Differentially Private Continual Learning

arXiv: https://arxiv.org/abs/1902.06497 · HTML: https://mlsanity.com/paper/1902.06497/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [Continual Learning with Differential Privacy](https://mlsanity.com/paper/2110.05223/) (2021)
- [Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning](https://mlsanity.com/paper/2207.12831/) (2022)
- [Forgetting, plasticity, and co-observation: a third facet of continual learning](https://mlsanity.com/paper/2608.18803/) (2026)

## Abstract (arXiv)

Catastrophic forgetting can be a significant problem for institutions that must delete historic data for privacy reasons. For example, hospitals might not be able to retain patient data permanently. But neural networks trained on recent data alone will tend to forget lessons learned on old data. We present a differentially private continual learning framework based on variational inference. We estimate the likelihood of past data given the current model using differentially private generative models of old datasets.