Less is More: Revisiting the Gaussian Mechanism for Differential Privacy


Tianxi Ji, Texas Tech University; Pan Li, Case Western Reserve University


Differential privacy (DP) via output perturbation has been a de facto standard for releasing query or computation results on sensitive data. Different variants of the classic Gaussian mechanism have been developed to reduce the magnitude of the noise and improve the utility of sanitized query results. However, we identify that all existing Gaussian mechanisms suffer from the curse of full-rank covariance matrices, and hence the expected accuracy losses of these mechanisms equal the trace of the covariance matrix of the noise.

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