Local Differential Privacy for Data Clustering

Lisa Bruder; Mina Alishahi

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Paper

Local Differential Privacy for Data Clustering

Topics: Data Protection; Database Security and Privacy; Information Hiding; Privacy; Privacy Enhancing Technologies

In Proceedings of the 21st International Conference on Security and Cryptography SECRYPT - Volume 1, 820-825, 2024 , Dijon, France

Authors: Lisa Bruder ¹ and Mina Alishahi ²

Affiliations: ¹ Informatics Institute, University of Amsterdam, The Netherlands ; ² Department of Computer Science, Open Universiteit, The Netherlands

Keyword(s): Local Differential Privacy, Clustering, Privacy, Non-Interactive LDP.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Bruder, L. and Alishahi, M. (2024). Local Differential Privacy for Data Clustering. In Proceedings of the 21st International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-709-2; ISSN 2184-7711, SciTePress, pages 820-825. DOI: 10.5220/0012838800003767

@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 - SECRYPT},
year={2024},
pages={820-825},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012838800003767},
isbn={978-989-758-709-2},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Security and Cryptography - SECRYPT
TI - Local Differential Privacy for Data Clustering
SN - 978-989-758-709-2
IS - 2184-7711
AU - Bruder, L.
AU - Alishahi, M.
PY - 2024
SP - 820
EP - 825
DO - 10.5220/0012838800003767
PB - SciTePress