Governments across the world looking to implement Open Government Data (OGD) initiatives undergo many problems. One such problem is the risk to privacy from opening data sets as most of the data is at a microdata level which corresponds to specific individuals. A solution to such a predicament is the application of Statistical Disclosure Control (SDC) techniques on microdata. SDC methods anonymize microdata that reduces the risk of disclosure while also maintaining the value of the data. SDC methods can be applied by using software tools, however, these tools are designed from the perspective of experts or for the purpose of demonstration. Moreover, ongoing research has led to the slow progress in not only the development of these tools, but also their adoption. Resulting in limited support material and even smaller user base. As a consequence, individuals or organizations looking to adopt these tools to satisfy their data privacy objectives cannot use them. Out of many SDC tools, ARX is a stable application that equips its users with an arsenal of techniques to anonymize microdata sets. It also undergoes regular updates, thus keeping pace with the current developments in the field of SDC techniques. Despite this, ARX is not widely used due to its perceived complexity. This thesis addresses the problem of the complexity that is associated with ARX which makes it difficult to adopt them to anonymize their data sets. The thesis provides a solution to this problem by developing a prototype tool which reduces the complexity of SDC techniques through a simplified, user-friendly approach to data anonymization. The thesis does not aim to enhance privacy methods or improve the functionalities of already existing tools by proposing a replacement. The thesis tries to bridge the gap which implicitly occurs when privacy tools are designed from the perspective of experts. It is understood that the protection of private data should only be handled by experts. However, to build that expertise, people have to be introduced to simpler tools without being overwhelmed by the complexity that is immanent with concepts of SDC.