Protection against disclosure is a legal and ethical obligation for agencies releasing microdata files for public use. Consider a microdata sample of size $n$ from a finite population of size $\bar{n}=n+λn$, with $λ>0$, such that each record contains two disjoint types of information: identifying categorical information and sensitive information. Any decision about releasing data is supported by the estimation of measures of disclosure risk, which are functionals of the number of sample records with a unique combination of values of identifying variables. The most common measure is arguably the number $τ_{1}$ of sample unique records that are population uniques. In this paper, we first study nonparametric estimation of $τ_{1}$ under the Poisson abundance model for sample records. We introduce a class of linear estimators of $τ_{1}$ that are simple, computationally efficient and scalable to massive datasets, and we give uniform theoretical guarantees for them. In particular, we show that they provably estimate $τ_{1}$ all of the way up to the sampling fraction $(λ+1)^{-1}\propto (\log n)^{-1}$, with vanishing normalized mean-square error (NMSE) for large $n$. We then establish a lower bound for the minimax NMSE for the estimation of $τ_{1}$, which allows us to show that: i) $(λ+1)^{-1}\propto (\log n)^{-1}$ is the smallest possible sampling fraction; ii) estimators' NMSE is near optimal, in the sense of matching the minimax lower bound, for large $n$. This is the main result of our paper, and it provides a precise answer to an open question about the feasibility of nonparametric estimation of $τ_{1}$ under the Poisson abundance model and for a sampling fraction $(λ+1)^{-1}<1/2$.