# Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review
## 6 Promising Future Research and Development Directions  
In this section, we present promising research and development directions for future work. Figure 10 presents the list of promising directions that require more work from the research and development point of view. For example, there is a serious lack of methods that can be applied to poor-quality data. In some cases, the data sensed from some devices such as sensors may contain errors, and therefore, applying anonymity to them directly may lead to the wrong data mining results. To this end, some new methods were developed to add noise to true values only, leading to lowering errors in histogram generation, as well as generic data mining results [146]. Some works have also explored the impact of data quality on privacy in real-time applications and crowdsensing scenarios [147]. Recently, some data-centric developments have been made to augment the performance of AI applications involving fewer data [148]. Hence, it is vital to adopt data-centric practices in the privacy domain to develop more secure methods (data quality-aware anonymization) in the future era. In the coming years, AI models will be applied to every sector, and therefore, it is imperative to devise secure methods for privacy preservation in AI systems [149]. To this end, amalgamating anonymization methods with AI systems is an attractive area of research for the near future. Privacy and utility optimization is a long-standing problem in the privacy domain [150]. Hence, it is vital to devise methods that can provide strict privacy guarantees without compromising usefulness in data-sharing scenarios. Figure 10: Promising direction for research or development in future endeavors. Based on our recent work [151], we found that AI models can improve some critical parts of the conventional anonymization methods. Therefore, it is vital to amalgamate the AI methods with the traditional anonymization methods to secure personal data effectively. In the coming years, hybrid anonymization methods may be needed to preserve the privacy of data enclosed in multiple formats. In the past, most anonymization methods anonymized data without improving their structure (e.g., without balancing the distributions), leading to poor data quality and poor privacy guarantees. In the future, it is vital to improve/augment data before its anonymization, particularly when the quality of the data is poor [152]. Data augmentation-based anonymization methods can improve the quality of the data, leading to better data mining and analytical results. In most anonymization methods, the computing complexity rises with either the horizontal or vertical expansion of data, and therefore, it is extremely important to reduce computing overheads when the underlying data to be anonymized are large [153, 154]. In future endeavors, devising low-cost methods (e.g., least computing and space complexity) for diverse data styles (e.g., graphs, images, text, etc.) is a vibrant area of research. Finally, there is a lack of metrics that can correctly capture the level of privacy and utility [155]. Hence, it is imperative to devise formally verified evaluation metrics in future endeavors. Finally, there is an increasing focus on developing clustering-based anonymization to improve the shortcomings of traditional anonymization methods [41, 156]. However, there are various critical problems with these methods such as poor convergence, a substantial number of iterations, and higher computing overheads. Hence, improving the technical status, applicability, and convergence criteria is a vibrant area of research for the future. Figure 11 provides a detailed overview of the current challenges in the privacy domain and new research topics about preserving the privacy of personnel data. Due to the rapid rise in personal data, privacy issues are becoming more evident [157, 158]. Therefore, robust, efficient, and low-cost anonymization methods are necessary to preserve privacy in futuristic applications.