Fingerprinting of data is a method to embed a traceable marker into the data to identify which specific recipient a certain copy of the data set has been released to. This is crucial for releasing data sets to third parties, especially if the release involves a fee, or if the data contains sensitive information due to which further sharing and potential subsequent leaks should be discouraged and deterred from. Fingerprints generally involve distorting the data set to a certain degree, in a trade off to preserve the utility of the data versus the robustness and traceability of the fingerprint. In this paper, we will thus compare several approaches for fingerprinting for their robustness against various types of attacks, such as subset or collusion attacks. We further evaluate the effects the fingerprinting has on the utility of the datasets, specifically for Machine Learning tasks.