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• W2781521040 abstract "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 $(epsilon,delta)$-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." @default.
• W2781521040 created "2018-01-12" @default.
• W2781521040 creator A5007869327 @default.
• W2781521040 creator A5015619835 @default.
• W2781521040 creator A5042307561 @default.
• W2781521040 creator A5066257035 @default.
• W2781521040 date "2018-01-02" @default.
• W2781521040 modified "2023-09-27" @default.
• W2781521040 title "MVG Mechanism: Differential Privacy under Matrix-Valued Query" @default.
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