# Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review
## 3 Discussion on Attribute-Centric Privacy-Preserving Methods  
Recently, many attribute-centric privacy methods were developed to secure personal data from adversaries while sustaining higher utility in anonymized data. These methods exploit characteristics of the underlying data to be anonymized and perform the required operation to accomplish the conflicting goals. In some cases, attribute-centric privacy methods were used to restrict changes in the anonymization process [58, 59]. In some cases, these methods were used to clean the data from the perspective of outliers, missing values, and/or redundant records to increase data utility [60]. Table 2 presents the technical details and comparisons of recently developed attribute-centric privacy methods. Each method listed in Table 2 has used a different mechanism to perform anonymization of the real data. The commonly used mechanisms in these studies are micro-aggregation, clustering, differential privacy, feature-aware anonymization, fuzzy \(c\)-means clustering, diverse grouping, chaos and perturbations, similarity-aware clustering, and generation of fair equivalence classes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Proposed Approach** & **Challenge (s)** & **Benefits** & **Contribution (s)** & **Reference** \\ \hline FL-based micro aggregation & Privacy–utility trade-off & Privacy in IIoT domains & Strong defense against recent privacy threats & [PERSON] et al. [46] \\ \hline Anonymization based techniques & Security and privacy challenges & Strong privacy of health data & Uncover privacy and security needs & [PERSON] et al. [47] \\ \hline Similarity-based analysis & Privacy in medical diagnosis & Strong privacy in cloud setting & A low-cost privacy-preserving framework & [PERSON] et al. [48] \\ \hline Blockchain-based system & Privacy protection of real data & Privacy of sensors data & A robust defense mechanism & [PERSON] et al. [49] \\ \hline ML + CPD method & Privacy–utility trade-off & Application in SSA and MSA scenarios & A hybrid method for PPDP & [PERSON] et al. [50] \\ \hline PPDSM and PPDM methods & Privacy in data mining & Protection of confidential data & Analysis of various methods & [PERSON] et al. [51] \\ \hline CV autoencoders & Identity protection in image data & Privacy of image data & Low cost perturbation methods & [PERSON] et al. [52] \\ \hline Blockchain-based system & Privacy of virus-infected users & Virus control and mitigation & Effective approach for privacy protection & [PERSON] et al. [53] \\ \hline Archive and data & Disclosure of sensitive commons & Privacy of genomics data & Proposed ways to address privacy issues & [PERSON] et al. [54] \\ \hline PPQE method & Privacy of confidential data & Responsible use of data & Reliable perturbation methods & [PERSON] et al. [55] \\ \hline DPView system & High-dimensional data handling & Better utility of SD & Data curation with privacy & [PERSON] et al. [56] \\ \hline DP model & Missing values handling & Informative analysis of COVID-19 data & Curating better data & [PERSON] et al. [57] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary and comparisons of most recent and SOTA privacy-preserving methods.