One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitation
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Abstract (arXiv)
Local differential privacy~(LDP) is an information-theoretic privacy definition suitable for statistical surveys that involve an untrusted data curator. An LDP version of quasi-maximum likelihood estimator~(QMLE) has been developed, but the existing method to build LDP QMLE is difficult to implement for a large-scale survey system in the real world due to long waiting time, expensive communication cost, and the boundedness assumption of derivative of a log-likelihood function. We provided an alternative LDP protocol without those issues, which is potentially much easily deployable to a large-scale survey. We also provided sufficient conditions for the consistency and asymptotic normality and limitations of our protocol. Our protocol is less burdensome for the users, and the theoretical guarantees cover more realistic cases than those for the existing method.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 36 references that are in the map. First posted on arXiv 15 Feb 2022.
Builds on
16 of 36 references.
References · 16
- 2016Deep Learning with Differential Privacycited by 2,682
- 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 262
- 2017Collecting Telemetry Data Privatelycited by 147
- 2018Protection Against Reconstruction and Its Applications in Private Federated Learningcited by 127
- 2014Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Boundscited by 104
- 2016Discrete Distribution Estimation under Local Privacycited by 58
- 2019Collecting and Analyzing Multidimensional Data with Local Differential Privacycited by 46
- 2015Local, Private, Efficient Protocols for Succinct Histogramscited by 35
- 2020Fisher information under local differential privacycited by 22
- 2018Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Casecited by 19
- 2019Privacy-preserving parametric inference: a case for robust statisticscited by 17
- 2015Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionariescited by 16
4 more
- 2017Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possiblecited by 12
- 2017Local Private Hypothesis Testing: Chi-Square Testscited by 10
- 2018Noninteractive Locally Private Learning of Linear Models via Polynomial Approximationscited by 7
- 2018Comparing Population Means under Local Differential Privacy: with Significance and Powercited by 5
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Citing papers per year: 2026: 1.
1 paper.