A Note on Sanitizing Streams with Differential Privacy

TL;DR

This paper explores methods for sanitizing data streams using low-memory algorithms to ensure privacy while preserving key statistical properties, addressing challenges in streaming data privacy.

Contribution

It introduces a streaming perspective to data sanitization with differential privacy, focusing on low-memory algorithms for real-time privacy-preserving data stream processing.

Findings

Proposes new algorithms for streaming data sanitization

Demonstrates effectiveness in preserving statistical properties

Addresses challenges of low-memory privacy algorithms

Abstract

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.