Collaboration and data sharing have become core elements of biomedical research. Especially when sensitive data from distributed sources are linked, privacy threats have to be considered. Statistical disclosure control allows the protection of sensitive data by introducing fuzziness. Reduction of data quality, however, needs to be balanced against gains in protection. Therefore, tools are needed which provide a good overview of the anonymization process to those responsible for data sharing. These tools require graphical interfaces and the use of intuitive and replicable methods. In addition, extensive testing, documentation and openness to reviews by the community are important. Existing publicly available software is limited in functionality, and often active support is lacking. We present ARX, an anonymization tool that i) implements a wide variety of privacy methods in a highly efficient manner, ii) provides an intuitive cross-platform graphical interface, iii) offers a programming interface for integration into other software systems, and iv) is well documented and actively supported.