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


Rizwan M., Hawbani A., Xingfu W., Anjum A., ANGIN ÜLKÜER P., SEVER Y., ...Daha Fazla

IEEE Transactions on Big Data, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1109/tbdata.2024.3495497
  • Dergi Adı: IEEE Transactions on Big Data
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: 1:M microdata, Big Data, electronic health records, Internet of Things, k-anonymity, privacy of data
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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