# Publishing Anonymized Set-Valued Data via Disassociation towards Analysis
## 6 Conclusion
Despite the perturbation and noise added to the support of itemsets, probabilistic wheel re-association is able to generate synthetic datasets respecting the overall data representation of the itemsets and association rules in the data. This is extremely interesting for prediction and decision-making analysis, as statistical exploration will lead to a very close hypothesis. On another side, when applying the same privacy constraints for disassociating the original dataset, the probabilistic wheel re-association algorithm generates re-associated datasets almost at the same distance from the original dataset. This reflects the fact that our approach respects the privacy imposed through the process. We can conclude that probabilistic wheel re-association is a faithful algorithm for knowledge extraction and data analysis over anonymized set-valued datasets. The preservation of predefined utility rules is necessary when we want to ensure their representation over a threshold. We run a set of experiments with the utility guided ant-based clustering algorithm, UGAC, to see how well can it preserve the utility rules. UGAC is compared with the classical clustering technique, \(k\)-means, and normal horizontal disassociation for various properties of utility rules. The results show that UGAC, compared to the other two solutions, is able to decrease the information loss for the utility rules without increasing the information loss of other associations in the cluster. Combining the two solutions, we can say that knowledge extraction and data analysis is exceptionally valid on anonymized datasets when transformed into neighbors to the original dataset. In future work, we intend to use the Tree Edit Distance on the set of generated neighbor datasets, to decide which re-association is the most representative of the datasets for publication, being the centroid, or in other words the least distance to all the other datasets. We will investigate our approach with other anonymization techniques for publishing datasets and generalize the mathematical evaluation of the utility-privacy trade-off for future uses of the dataset in machine learning algorithms. **Author Contributions:** Conceptualization, N.A. and J.-F.C. ; methodology, N.A. and J.-F.C. ; software, N.A. and J.-F.C. and B.A.B. and L.P.; validation, J.-F.C. and B.A.B. and L.P.; formal analysis, N.A. and J.-F.C. ; investigation, N.A. and J.-F.C. ; resources, J.-F.C. and B.A.B. and L.P.; writing--original draft preparation, N.A. and J.-F.C. ; writing--review and editing, N.A. and J.-F.C. ; resources, J.-F.C. and B.A.B. and L.P.; supervision, J.-F.C. and B.A.B. and L.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This work is funded by the Labex ACTION program (contract ANR-11-LABX-01-01), by the Interreg RESponSE project, by the Hubert CUEin CEDRE Programme number 40283 YK, by the National Council for Scientific Research in Lebanon CNRS-L, and by the Antonine University. Computations have been performed on the supercomputer facilities of the Mesocentre de calcul de Franche-Comte. **Conflicts of Interest:** The authors declare no conflict of interest.