MVG Mechanism: Differential Privacy under Matrix-Valued Query
This addresses the problem of inefficient privacy protection for matrix-valued data in differential privacy, offering a domain-specific improvement.
The paper tackles the suboptimal performance of existing differential privacy mechanisms for matrix-valued queries by proposing the Matrix-Variate Gaussian (MVG) mechanism, which adds matrix-valued noise and experimentally shows it outperforms four previous state-of-the-art approaches and provides utility comparable to non-private baselines.
Differential privacy mechanism design has traditionally been tailored for a scalar-valued query function. Although many mechanisms such as the Laplace and Gaussian mechanisms can be extended to a matrix-valued query function by adding i.i.d. noise to each element of the matrix, this method is often suboptimal as it forfeits an opportunity to exploit the structural characteristics typically associated with matrix analysis. To address this challenge, we propose a novel differential privacy mechanism called the Matrix-Variate Gaussian (MVG) mechanism, which adds a matrix-valued noise drawn from a matrix-variate Gaussian distribution, and we rigorously prove that the MVG mechanism preserves $(ε,δ)$-differential privacy. Furthermore, we introduce the concept of directional noise made possible by the design of the MVG mechanism. Directional noise allows the impact of the noise on the utility of the matrix-valued query function to be moderated. Finally, we experimentally demonstrate the performance of our mechanism using three matrix-valued queries on three privacy-sensitive datasets. We find that the MVG mechanism notably outperforms four previous state-of-the-art approaches, and provides comparable utility to the non-private baseline.