---
res:
  bibo_abstract:
  - "In this paper, we study the problem of estimating the unknown mean θ of a unit
    variance Gaussian distribution in a locally differentially private (LDP) way.
    In the high-privacy regime (ϵ≤1\r\n), we identify an optimal privacy mechanism
    that minimizes the variance of the estimator asymptotically. Our main technical
    contribution is the maximization of the Fisher-Information of the sanitized data
    with respect to the local privacy mechanism Q. We find that the exact solution
    Qθ,ϵ of this maximization is the sign mechanism that applies randomized response
    to the sign of Xi−θ, where X1,…,Xn are the confidential iid original samples.
    However, since this optimal local mechanism depends on the unknown mean θ, we
    employ a two-stage LDP parameter estimation procedure which requires splitting
    agents into two groups. The first n1 observations are used to consistently but
    not necessarily efficiently estimate the parameter θ by θn1~\r\n. Then this estimate
    is updated by applying the sign mechanism with θ~n1 instead of θ\r\n to the remaining
    n−n1 observations, to obtain an LDP and efficient estimator of the unknown mean.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Nikita
      foaf_name: Kalinin, Nikita
      foaf_surname: Kalinin
      foaf_workInfoHomepage: http://www.librecat.org/personId=4b14526e-14d2-11ed-ba64-c14c9553d137
  - foaf_Person:
      foaf_givenName: Lukas
      foaf_name: Steinberger, Lukas
      foaf_surname: Steinberger
  bibo_volume: 258
  dct_date: 2025^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2640-3498
  dct_language: eng
  dct_publisher: ML Research Press@
  dct_title: Efficient estimation of a Gaussian mean with local differential privacy@
...