# A Note on Sanitizing Streams with Differential Privacy

arXiv: https://arxiv.org/abs/2111.13762 · HTML: https://mlsanity.com/paper/2111.13762/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2021.

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

- [On Differential Privacy and Adaptive Data Analysis with Bounded Space](https://mlsanity.com/paper/2302.05707/) (2023)

## Abstract (arXiv)

The literature on data sanitization aims to design algorithms that take an input dataset and produce a privacy-preserving version of it, that captures some of its statistical properties. In this note we study this question from a streaming perspective and our goal is to sanitize a data stream. Specifically, we consider low-memory algorithms that operate on a data stream and produce an alternative privacy-preserving stream that captures some statistical properties of the original input stream.