# Enhancing Utility in Anonymized Data against the Adversary's Background Knowledge
## 2 Related Work
### Creation of the Initial QI Groups
We set J = 0.8, so the BK-based clustering creates clusters with maximum sizes, and \(t\)-\(k\)-Utility moves more of the records among QI groups to meet the privacy requirements. The execution time of algorithms on the BKseq dataset is displayed in Figure 5 when the \(k\) value varies from 3 to 20. Our algorithm and \(k\)-primacy have time complexity with the order o(\(n^{2}\)\(logn\)) to create the groups in BK-based clustering. We see that the runtime decreases as \(k\) increases. It can be seen that BKA-JS is more efficient than our proposed algorithm due to a lower runtime. UHRA and \(k\)-primacy have a similar runtime. ## 6 Conclusions

This study proposed an algorithm for data publishing and dissemination in which the adversary has the BK to re-identify an individual in the anonymized data. However, privacy requirements prevent the BK attacks and identity and attribute disclosures. A hierarchical algorithm, UHRA, was proposed to satisfy the privacy requirements, including two steps. First, UHRA uses agglomerative clustering to prevent background knowledge attacks that consider correlations between sensitive and QI attributes. Then, \(t\)-closeness and \(k\)-anonymity are applied to the QI groups, and records are moved between QI groups

Figure 4: Execution time (in milliseconds with log 10 scale) of our algorithm with \(t\)= 0.5 and \(k\) = 5 regarding various J values on the BKseq dataset. Figure 5: Execution time (in milliseconds with log 10 scale) of our algorithm when J = 0.8, \(t\) = 0.5, and the \(k\) value varies on the BKseq dataset

to increase data utility as long as the privacy requirements are not violated. The evaluation results confirm the higher data utility compared to other algorithms. Experimental results show that the proposed algorithm causes lower privacy loss and information loss. An increase in the \(k\) value increases the size of the QI groups, and the information loss rises too. When the J and \(t\) values decrease, the number of unpublished records increases. For future studies, we suggest that the proposed algorithm be evaluated in re-publishing scenarios. In data re-publishing, an adversary can use the combination of information from different releases over time. Data correlation can lead to a privacy breach. Conceptualization, F.A. and M.H.S. ; Methodology, F.A. ; Formal analysis, F.A and S.R. ; Investigation, F.A. and [PERSON]; Data curation, F.A., A.A. and S.R. ; Writing--original draft, F.A. ; Writing--review & editing, F.A., [PERSON] and M.H.S. ; Supervision, A.A.; Funding acquisition, S.R. All authors have read and agreed to the published version of the manuscript. The authors acknowledge TU Wien Bibliothek for financial support through its Open Access Funding Programme. Not applicable. Publicly available datasets are used in this study: Adult Dataset. 2022. Available online: [[https://archive.ics.uci.edu/ml/datasets/Adult](https://archive.ics.uci.edu/ml/datasets/Adult)]([https://archive.ics.uci.edu/ml/datasets/Adult](https://archive.ics.uci.edu/ml/datasets/Adult)) (accessed on 22 August 2022). The authors acknowledge TU Wien Bibliothek for supporting this research. The authors declare no conflict of interest. ## References

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