A Note on Sanitizing Streams with Differential Privacy
Haim Kaplan, Uri Stemmer

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.
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Mobile Crowdsensing and Crowdsourcing
