Paper Info
Title
MVG Mechanism: Differential Privacy under Matrix-Valued Query.
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 (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 can notably outperforms four previous state-of-the-art approaches, and provides comparable utility to the non-private baseline.
Year
DOI
Venue
2018
10.1145/3243734.3243750
ACM Conference on Computer and Communications Security
Keywords
DocType
Volume
differential privacy, matrix-valued query, matrix-variate Gaussian, directional noise, MVG mechanism
Conference
abs/1801.00823
ISSN
ISBN
Citations 
Thee Chanyaswad, Alex Dytso, H. Vincent Poor, and Prateek Mittal. 2018. MVG Mechanism: Differential Privacy under Matrix-Valued Query. In 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS'18)
978-1-4503-5693-0
2
PageRank 
References 
Authors
0.35
52
4
Name
Order
Citations
PageRank
Thee Chanyaswad182.63
Alex Dytso24520.03
H. V. Poor3254111951.66
Prateek Mittal4113470.19