A cluster-based approach for distributed anonymisation of vertically partitioned data

被引：0

作者：

Xenakis, Antonios ^{[1
]}

Chen, Zhiyuan ^{[1
]}

Karabatis, George ^{[1
]}

机构：

[1] Department of Information Systems, University of Maryland, Baltimore County (UMBC), Baltimore, MD

来源：

International Journal of Web Engineering and Technology | 2024年 / 19卷 / 04期

关键词：

cluster-based anonymisation; differential privacy; distributed anonymisation; K-anonymity; privacy;

D O I：

10.1504/IJWET.2024.143360

中图分类号：

学科分类号：

摘要：

In modern organisations, data is often spread across different sites, posing challenges for effective analysis. Transferring data to a centralised server may jeopardise privacy and leak sensitive/proprietary information. Therefore, organisations hesitate adopting this solution despite its potential to fully utilise, and analyse the data, for better decision making. Current approaches concentrate on distributed privacy-preserving techniques for data analysis, where data does not leave each site, but incurs substantial computational and communication overhead. This paper focuses on distributed data that is anonymised on site, then merged and sent to a centralised server for analysis. Two new approaches on cluster-based distributed anonymisation are introduced for vertically partitioned data, one based on distributed coordinated anonymisation, and the other based on top-down distributed anonymisation, resulting in low initial onsite anonymisation overhead. Experiments show these approaches preserve data privacy with very minor loss of utility of anonymised data and impose minimal computational overhead. © 2024 Inderscience Enterprises Ltd.

引用

页码：397 / 420

页数：23

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