• Corpus ID: 224704510

# Locally Differentially Private Analysis of Graph Statistics

@inproceedings{Imola2020LocallyDP,
title={Locally Differentially Private Analysis of Graph Statistics},
author={Jacob Imola and Takao Murakami and Kamalika Chaudhuri},
booktitle={USENIX Security Symposium},
year={2020}
}
• Published in USENIX Security Symposium 17 October 2020
• Computer Science, Mathematics
Differentially private analysis of graphs is widely used for releasing statistics from sensitive graphs while still preserving user privacy. Most existing algorithms however are in a centralized privacy model, where a trusted data curator holds the entire graph. As this model raises a number of privacy and security issues -- such as, the trustworthiness of the curator and the possibility of data breaches, it is desirable to consider algorithms in a more decentralized local model where no server…
22 Citations

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