One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitation
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:2762-2783, 2022.
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 alternative LDP protocols without those issues, which are 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.