Fundamental Limits of Obfuscation for Linear Gaussian Dynamical Systems: An Information-Theoretic Approach
This work addresses privacy concerns in dynamical systems for applications like control or data analysis, but it is incremental as it extends information-theoretic methods to a specific class of systems.
The paper tackles the problem of privacy-distortion tradeoffs in linear Gaussian dynamical systems by deriving analytical formulas for optimal obfuscation, showing that privacy masks should be colored Gaussian with power spectra shaped based on system and noise properties.
In this paper, we study the fundamental limits of obfuscation in terms of privacy-distortion tradeoffs for linear Gaussian dynamical systems via an information-theoretic approach. Particularly, we obtain analytical formulas that capture the fundamental privacy-distortion tradeoffs when privacy masks are to be added to the outputs of the dynamical systems, while indicating explicitly how to design the privacy masks in an optimal way: The privacy masks should be colored Gaussian with power spectra shaped specifically based upon the system and noise properties.