A rate-disortion perspective on local differential privacy

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Extremal Mechanisms for Local Differential Privacy (2014, background): “However, connections to differential privacy have been studied only indirectly through comparisons to how much distortion is incurred under the two notions of privacy (Sarwate and Sankar, 2014; Wang et al., 2014a).”
Locally differentially-private distribution estimation (2016, background): “Our privacy requirement will be based on the notion of -local differential privacy [11], which is inherited from the 2016 IEEE International Symposium on Information Theory”
Randomized requantization with local differential privacy (2016, background): “A channel Q is ε-locally differentially private[9, 10] if”
On the Relation Between Identifiability, Differential Privacy, and Mutual-Information Privacy (2016, background): “Sarwate and Sankar [31] showed that the result in [33] indicates a one direction bound between the optimal differential privacy and the optimal mutual information given the same distortion constraint.”