Enhancing correlated big data privacy using differential privacy and machine learning

Abstract

Data are often correlated in real-world datasets. Existing data privacy algorithms did not consider data correlation an inherent property of datasets. This data correlation caused privacy leakages that most researchers left unnoticed. Such privacy leakages are often caused by homogeneity, background knowledge, and linkage attacks, and the probability of such attacks increases with the magnitude of correlation among data. This problem further got magnified by the large size of real-world datasets, and we refer to these large datasets as ’Big Data.’ Several researchers proposed algorithms using machine learning models, correlation analysis, and data privacy algorithms to prevent privacy leakages due to correlation in large-sized data. The current proposed work first analyses the correlation among data. We studied the Mutual Information Correlation analysis technique and the distance correlation analysis technique for data correlation analysis. We found out distance correlation analysis technique to be more accurate for high-dimensional data. It then divides the data into blocks using the correlation computed earlier and applies the differential privacy algorithm to ensure the data privacy expectations. The results are derived based upon multiple parameters such as data utility, mean average error, variation with data size, and privacy budget values. The results showed that the proposed methodology provides better data utility when compared to the works of other researchers. Also, the data privacy commitments offered by the proposed method are comparable to the other results. Thus, the proposed methodology gives a better data utility while maintaining the required data privacy commitments.