An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata

Date

2024-01-01

Author

Rizwan, Muhammad
Hawbani, Ammar
Xingfu, Wang
Anjum, Adeel
Angın, Pelin
Sever, Yiğit
Chen, Sanchuan
Zhao, Liang
Al-Dubai, Ahmed

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

12
views

0
downloads

Cite This

A data publishing deal conducted with anonymous microdata can preserve the privacy of people. However, anonymizing data with multiple records of an individual (1:M dataset) is still a challenging problem. After anonymizing the 1:M microdata, the vertical correlation can be exploited to launch privacy attacks. In this paper, a novel privacy preserving model lc, ls-ANGEL is proposed. To validate the new model, two privacy attacks are presented, namely, a Vertical correlation attack (Vc0) and a Vulnerable sensitive attribute attack (Vsa) on 1:M datasets, which breach the privacy of individuals. Furthermore, the proposed model is examined through High-Level Petri Nets (HLPNs). Our experiments on three real-world datasets;'INFORMS','YOUTUBE', and 'IMDb' demonstrate that the proposed model outperforms the state-of-the-art models. Our practices and lessons learned in this work can direct future concrete steps towards Multiple Sensitive Attributes, where we can expand the proposed model to dynamic datasets

Subject Keywords

1:M microdata, Big Data, electronic health records, Internet of Things, k-anonymity, privacy of data

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85209630716&origin=inward
https://hdl.handle.net/11511/112813

Collections

Department of Computer Engineering, Article