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

Rizwan, Muhammad; Hawbani, Ammar; Xingfu, Wang; Anjum, Adeel; Angin, Pelin; Sever, Yigit; Chen, Sanchuan; Zhao, Liang; Al-Dubai, Ahmed

Abstract

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

Citation

Rizwan, M., Hawbani, A., Xingfu, W., Anjum, A., Angin, P., Sever, Y., Chen, S., Zhao, L., & Al-Dubai, A. (online). An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata. IEEE Transactions on Big Data, https://doi.org/10.1109/TBDATA.2024.3495497

• Journal Article Type: Article
• Acceptance Date: Oct 31, 2024
• Online Publication Date: Nov 11, 2024
• Deposit Date: Nov 1, 2024
• Publicly Available Date: Nov 11, 2024
• Journal: IEEE Transactions on Big Data
• Electronic ISSN: 2332-7790
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/TBDATA.2024.3495497
• Keywords: Internet of Things, big data, electronic health records, privacy of data, k-anonymity, 1:M microdata