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.
@article{arxiv.2111.13762,
title = {A Note on Sanitizing Streams with Differential Privacy},
author = {Haim Kaplan and Uri Stemmer},
journal= {arXiv preprint arXiv:2111.13762},
year = {2021}
}