ρ-Kazanım: Mahremiyet Korumalı Fayda Temelli Veri Yayınlama Modeli
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Data privacy is a difficult problem that tries to find the best balance between the privacy risks of data owners and the utility of data sharing to the third parties. Anonymization is the most commonly applied technique to overcome data privacy problems. The equivalence classes, the natural outcome of anonymization process, are classified according to the data utility in two main categories: Utility and Outlier Equivalence Classes (UEC, OEC). The utility equivalence class contains records that have been suppressed by anonymization techniques for privacy concerns. Meanwhile, the outlier equivalence class contains records that have been fully suppressed by anonymization techniques resulting in no data utility. In this study, ρ-Gain model, which focus on the effect of outlier equivalence class for increasing data utility, was proposed. In the proposed model, k-Anonymity, ℓ-Diversity and t-Closeness privacy models were used together with ρ-iterations to reduce the privacy risks. The Average Equivalence Class metric was used to measure data utility. According to the findings obtained from the study, the ρ-Gain model improved the data utility, but did not cause a significant negative impact on privacy risk estimates. With the use of the proposed ρ-Gain model as an anonymization technique, we have shown that the data utility has improved while keeping the data privacy risk with no significant change.