Local Differential Privacy for Data Clustering

Lisa Bruder; Mina Alishahi

doi:10.5220/0012838800003767

Local Differential Privacy for Data Clustering

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 21st International Conference on Security and Cryptography, SECRYPT 2024
Editors	Sabrina De Capitani Di Vimercati, Pierangela Samarati
Publisher	SCITEPRESS-Science and Technology Publications, Lda.
Pages	820-825
Number of pages	6
ISBN (Electronic)	9789897587092
DOIs	https://doi.org/10.5220/0012838800003767
Publication status	Published - 2024
Event	21st International Conference on Security and Cryptography, SECRYPT 2024 - Dijon, France Duration: 8 Jul 2024 → 10 Jul 2024 Conference number: 21

Publication series

Series	Proceedings of the International Conference on Security and Cryptography
ISSN	2184-7711

Conference

Conference	21st International Conference on Security and Cryptography, SECRYPT 2024
Abbreviated title	SECRYPT 2024
Country/Territory	France
City	Dijon
Period	8/07/24 → 10/07/24

Keywords

Clustering
Local Differential Privacy
Non-Interactive LDP
Privacy

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10.5220/0012838800003767Licence: CC BY-NC-ND

Cite this

Bruder, Lisa ; Alishahi, Mina. / Local Differential Privacy for Data Clustering. Proceedings of the 21st International Conference on Security and Cryptography, SECRYPT 2024. editor / Sabrina De Capitani Di Vimercati ; Pierangela Samarati. SCITEPRESS-Science and Technology Publications, Lda., 2024. pp. 820-825 (Proceedings of the International Conference on Security and Cryptography).

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title = "Local Differential Privacy for Data Clustering",

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.",

keywords = "Clustering, Local Differential Privacy, Non-Interactive LDP, Privacy",

author = "Lisa Bruder and Mina Alishahi",

note = "Publisher Copyright: {\textcopyright} 2024 by SCITEPRESS – Science and Technology Publications, Lda.; 21st International Conference on Security and Cryptography, SECRYPT 2024, SECRYPT 2024 ; Conference date: 08-07-2024 Through 10-07-2024",

year = "2024",

doi = "10.5220/0012838800003767",

language = "English",

series = "Proceedings of the International Conference on Security and Cryptography",

publisher = "SCITEPRESS-Science and Technology Publications, Lda.",

pages = "820--825",

editor = "{Di Vimercati}, {Sabrina De Capitani} and Pierangela Samarati",

booktitle = "Proceedings of the 21st International Conference on Security and Cryptography, SECRYPT 2024",

address = "Portugal",

}

Bruder, L & Alishahi, M 2024, Local Differential Privacy for Data Clustering. in SDC Di Vimercati & P Samarati (eds), Proceedings of the 21st International Conference on Security and Cryptography, SECRYPT 2024. SCITEPRESS-Science and Technology Publications, Lda., Proceedings of the International Conference on Security and Cryptography, pp. 820-825, 21st International Conference on Security and Cryptography, SECRYPT 2024, Dijon, France, 8/07/24. https://doi.org/10.5220/0012838800003767

Local Differential Privacy for Data Clustering. / Bruder, Lisa; Alishahi, Mina.
Proceedings of the 21st International Conference on Security and Cryptography, SECRYPT 2024. ed. / Sabrina De Capitani Di Vimercati; Pierangela Samarati. SCITEPRESS-Science and Technology Publications, Lda., 2024. p. 820-825 (Proceedings of the International Conference on Security and Cryptography).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

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PY - 2024

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AB - 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.

KW - Clustering

KW - Local Differential Privacy

KW - Non-Interactive LDP

KW - Privacy

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DO - 10.5220/0012838800003767

M3 - Conference Article in proceeding

AN - SCOPUS:85202844889

T3 - Proceedings of the International Conference on Security and Cryptography

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EP - 825

BT - Proceedings of the 21st International Conference on Security and Cryptography, SECRYPT 2024

A2 - Di Vimercati, Sabrina De Capitani

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Y2 - 8 July 2024 through 10 July 2024

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Bruder L, Alishahi M. Local Differential Privacy for Data Clustering. In Di Vimercati SDC, Samarati P, editors, Proceedings of the 21st International Conference on Security and Cryptography, SECRYPT 2024. SCITEPRESS-Science and Technology Publications, Lda. 2024. p. 820-825. (Proceedings of the International Conference on Security and Cryptography). doi: 10.5220/0012838800003767

Local Differential Privacy for Data Clustering

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