In modern societies, opening data is playing a crucial role in innovations and economic growth. Public organizations and private enterprises constantly are collecting data. To support the growth of societies, these organizations and enterprises intend to be more active in data opening. However, disclosure of personal data is one of the main threats for data opening. Data transformation techniques for Statistical Disclosure Control (SDC) aim at removing personal data while maintaining the utility of the data at an acceptable level. Applying SDC methods always faces the struggle of maintaining a balance between data utility and personal disclosure risk. In this research, we investigate different options for a common set of transformations for protecting microdata. We study a set of common scenarios which target (or specify) two types of data environments (i.e., those with and without the original microdata sets) and two approaches for privacy protection (i.e., those based on normative heuristics and a formal approach). Employing ARX, we run a series of experiments to observe the behaviours of various measurement factors. At the end, we discuss the consequences of choosing each of the options that can be used by policymakers for opening privacy-sensitive microdata sets.