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Abstract
The integration of differential privacy (DP) mechanisms into public sector analytics has become increasingly critical in the era of high-dimensional data, where privacy preservation and analytical utility are often at odds. This paper provides a focused theoretical and empirical investigation of the effectiveness of prominent DP algorithms—particularly the Laplace and Gaussian mechanisms—within high-dimensional public datasets. We evaluate the performance trade-offs across varying privacy budgets and dimensionalities, using real-world census and health datasets. Our findings highlight that while noise calibration in high-dimensional settings preserves privacy, it often leads to significant utility degradation, necessitating smarter dimensionality reduction and adaptive noise distribution.
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