Tackling Privacy Concerns in Correlated Big Data: A Comprehensive Review with Machine Learning Insights

Data dependency has grown rapidly in an era where data has become a valuable global asset, resulting in highly correlated real-world datasets. In recent years, there has been an increasing emphasis on the critical importance of data privacy, highlighting the limitations of standard privacy protections when it comes to handling datasets that are correlated. The conventional privacy methods, which were previously sufficient in situations where there were no data connections, are now insufficient. Given the existence of data correlation, it is imperative to reassess privacy algorithms. Researchers have investigated the utilization of Data Correlation Analysis, a widely recognized technique in machine learning, to obtain more profound understanding of the connections inside data. Although research in the field of linked data privacy is sparse, this technique has produced positive results. Significantly, there is a lack of research on the privacy of correlated big data. Real-world datasets, which are commonly referred to as big data because of their large size, often display a notable level of data correlation. Therefore, it is crucial to comprehend everything and suggest efficient solutions for the privacy issues caused by correlated big data.