Local Differential Privacy for Data Clustering

Lisa Bruder, Mina Alishahi

2024

Abstract

This study presents an innovative framework that utilizes Local Differential Privacy (LDP) to address the challenge of data privacy in practical applications of data clustering. Our framework is designed to prioritize the protection of individual data privacy by empowering users to proactively safeguard their information before it is shared to any third party. Through a series of experiments, we demonstrate the effectiveness of our approach in preserving data privacy while simultaneously facilitating insightful clustering analysis.

Paper Citation

in Harvard Style

Bruder L. and Alishahi M. (2024). Local Differential Privacy for Data Clustering. In Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-709-2, SciTePress, pages 820-825. DOI: 10.5220/0012838800003767

in Bibtex Style

@conference{secrypt24,
author={Lisa Bruder and Mina Alishahi},
title={Local Differential Privacy for Data Clustering},
booktitle={Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2024},
pages={820-825},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012838800003767},
isbn={978-989-758-709-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Local Differential Privacy for Data Clustering
SN - 978-989-758-709-2
AU - Bruder L.
AU - Alishahi M.
PY - 2024
SP - 820
EP - 825
DO - 10.5220/0012838800003767
PB - SciTePress