The Laplace mechanism impact on the statistical characteristics of the original data

Differential privacy (DP) involves adding controlled noise to the input data or computational results which provides robust and provable privacy preservation but can have a significant impact on the statistical datasets properties with potentially influencing effect to their subsequent analysis. This feature forces a trade-off analysis between privacy and utility. The paper presents the research results of the DP Laplace mechanism impact on various data distributions and their statistical properties. The feature changes are studied in the context of three experiments for several key types of distributions while applying the Laplace mechanism with different values of the privacy budget (epsilon). The classical Laplace mechanism is compared with its extensions in the context of their effectiveness and the impact on different levels of feature correlation in the original data is considered. The results highlight the trade-offs between privacy and data utility and also provide recommendations for choosing suitable DP mechanisms for different scenarios.