Before revision:  Our analysis suggests that many other estimators that rely on nuisance parameters may have similar properties with the requirement of differential privacy  .
After revision:  Our analysis suggests that many other estimators that rely on nuisance parameters may have similar properties with the requirement of differential privacy . We show that identification becomes possible if the target parameter can be deterministically located within the random set. In that case, a full exploration of the random set of the weak limits of differentially private estimators can allow the data curator to select a sequence of instances of differentially private estimators converging to the target parameter in probability .