Privacy-Preserving Big Hierarchical Data Analytics via Co-Occurrence Analysis

Alfredo Cuzzocrea, Alfredo Cuzzocrea, Selim Soufargi

2024

Abstract

Nowadays, Big Data Analytics is gaining the momentum in both the academic and industrial research communities. In this context, the issue of performing such a critical process under tight privacy-preservation constraints plays the critical role of “enabling technology”. This paper, by perfectly aligning with the depicted paradigm, introduces and experimentally assesses Drill-CODA, an innovative framework that combines drill-across multidimensional big data analytics and co-occurrence analysis to finally achieve privacy-preservation during the analytical phase.

Paper Citation

in Harvard Style

Cuzzocrea A. and Soufargi S. (2024). Privacy-Preserving Big Hierarchical Data Analytics via Co-Occurrence Analysis. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-707-8, SciTePress, pages 93-103. DOI: 10.5220/0012767800003756

in Bibtex Style

@conference{data24,
author={Alfredo Cuzzocrea and Selim Soufargi},
title={Privacy-Preserving Big Hierarchical Data Analytics via Co-Occurrence Analysis},
booktitle={Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2024},
pages={93-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012767800003756},
isbn={978-989-758-707-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Privacy-Preserving Big Hierarchical Data Analytics via Co-Occurrence Analysis
SN - 978-989-758-707-8
AU - Cuzzocrea A.
AU - Soufargi S.
PY - 2024
SP - 93
EP - 103
DO - 10.5220/0012767800003756
PB - SciTePress