When applying outlier detection in settings where data is sensitive, mechanisms which guarantee the privacy of the underlying data are needed. The (k)-nearest neighbors ((k)-NN) algorithm is a simple and one of the most effective methods for outlier detection. So far, there have been no attempts made to develop a differentially private ((\epsilon)-DP) approach for (k)-NN based outlier detection. Existing approaches often relax the notion of (\epsilon)-DP and employ other methods than (k)-NN. We propose a method for (k)-NN based outlier detection by separating the procedure into a fitting step on reference inlier data and then apply the outlier classifier to new data. We achieve (\epsilon)-DP for both the fitting algorithm and the outlier classifier with respect to the reference data by partitioning the dataset into a uniform grid, which yields low global sensitivity. Our approach yields nearly optimal performance on real-world data with varying dimensions when compared to the non-private versions of (k)-NN.