Abstract
This chapter builds on the two-stage architecture for differentially private filtering, and presents mechanisms with better performance than the zero-forcing equalization mechanism, for the situation where we have some knowledge about the statistics of the privacy-sensitive input signals, which moreover are assumed to be stationary. The mechanisms described use as second stage in the architecture a Wiener filter, and the performance of the overall mechanism is then optimized, following the general methodology outlined in Chap. 3.
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Le Ny, J. (2020). Differentially Private Filtering for Stationary Stochastic Collective Signals. In: Differential Privacy for Dynamic Data. SpringerBriefs in Electrical and Computer Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-030-41039-1_4
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DOI: https://doi.org/10.1007/978-3-030-41039-1_4
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