Privacy- & Utility-Preserving Data Releases over Fragmented Data Using Individual Differential Privacy

Luis Del Vasto-Terrientes, Sergio Martínez, David Sánchez, David Sánchez

2025

Abstract

Data fragmentation is the process of splitting data into either attributes or records across multiple databases, thereby improving operational efficiency, minimizing processing requirements, and enhancing data privacy. However, under this approach, data aggregation becomes complex, particularly in environments where adherence to regulatory compliance is essential for organizational data analysis and decision-making tasks. Since the dataset held by each party may contain sensitive information, simply joining local datasets and releasing the aggregated result will inevitably reveal such sensitive information to other parties. Differential Privacy (DP) has become the de facto standard for data protection due to its rigorous notion of privacy. However, the strong privacy guarantees it offers result in a deterioration of data utility in several scenarios, such as data releases in either centralized or fragmented data scenarios. This paper explores the application of Individual Differential Privacy (iDP)—a formulation of DP conceived to better preserve data utility while still providing strong privacy guarantees to individuals—for data releases in either horizontally or vertically fragmented scenarios. In combination with individual ranking (IR) microaggregation, an iDP-IR privacy-preserving data release system is presented, in which multiple data owners can safely share datasets. Our experiments on the Adult and Wine Quality datasets demonstrate that the proposed system for fragmented data can provide reasonable information loss with robust ε privacy values.

Paper Citation

in Harvard Style

Vasto-Terrientes L., Martínez S. and Sánchez D. (2025). Privacy- & Utility-Preserving Data Releases over Fragmented Data Using Individual Differential Privacy. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 318-329. DOI: 10.5220/0013141200003899

in Bibtex Style

@conference{icissp25,
author={Luis Vasto-Terrientes and Sergio Martínez and David Sánchez},
title={Privacy- & Utility-Preserving Data Releases over Fragmented Data Using Individual Differential Privacy},
booktitle={Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP},
year={2025},
pages={318-329},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013141200003899},
isbn={978-989-758-735-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP
TI - Privacy- & Utility-Preserving Data Releases over Fragmented Data Using Individual Differential Privacy
SN - 978-989-758-735-1
AU - Vasto-Terrientes L.
AU - Martínez S.
AU - Sánchez D.
PY - 2025
SP - 318
EP - 329
DO - 10.5220/0013141200003899
PB - SciTePress