@article{BCO20,
  author    = {Leighton P. Barnes and
               Wei{-}Ning Chen and
               Ayfer {\"{O}}zg{\"{u}}r},
  title     = {Fisher information under local differential privacy},
  journal   = {CoRR},
  volume    = {abs/2005.10783},
  url = {https://arxiv.org/abs/2005.10783},
  year      = {2020},
  bibbase_note = {<div class="well well-small bibbase"><span class="bluecite">[BCÖ20]</span> Provides lower bounds for parameter estimation under $\ell_2$ loss for interactive protocols (blackboard model) under local privacy, and instantiate it to obtain tight bounds for Gaussian mean estimation, (sparse) Bernoulli mean estimation, and discrete distribution estimation. As in [BHÖ19], the lower bound framework is based on a Cramér–Rao/van Trees-type approach.},
  note = {Accepted to the IEEE Journal on Selected Areas in Information Theory (JSAIT).</div>}
}

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