Differential Privacy via Wavelet Transforms

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Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity (2018, methodology): “fully correlating noise across updates, they reduce the error to O(polylogd). (In related work Chan et al. [CSS11] describe a post-processing procedure that guarantees output consistency; Xiao et al. [XWG11] present a conceptually similar algorithm framed as a basis transformation.) We adapt these t”
Differential privacy via wavelet transforms (2009, background): “We refer the reader to our technical report [11] for the complete set of proofs.”
Minimax Filter: Learning to Preserve Privacy from Inference Attacks (2016, methodology): “Xiao et al. (2011) presented a differentially private range-counting method where they used wavelets to transform the data before adding noise.”
On the Power of Multiple Anonymous Messages (2019, unknown): “rly works on differentially private histograms [24, 33] apply naturally to range queries, though the approach of summing up histogram entries yields large errors for longer range queries. Xiao et al. [48] showed how to obtain differentially private range count queries by using Haar wavelets, while H”