## 2 Related Work  
This work takes inspiration from problem-solving-focused engineering disciplines. For this purpose, the design science research (DSR) methodology was followed, which has a problem-oriented approach. According to this methodology, an artifact based on an existing problem is created to solve the issue. Moreover, this study adopted the related information systems research framework for the proposed approach, as described by [PERSON] et al. [14]. For the literature review, this work implemented the systemic taxonomy for information retrieval from literature (STIRL) approach proposed by [PERSON] et al. [14]. This approach allows for a machine-learning-supported systematic literature review. Thus, it can also be applied to examine large volumes of research articles. Qualitatively highly maintainedonline research databases were selected as data sources [15; 16]. As shown in Table 1, the literature was selected using online literature databases and search strings. When selecting keywords, care was taken to ensure that they were broad enough to identify all relevant papers, but narrow enough to avoid returning too many articles and, thus, possibly corrupting the results. Since compliance is a subtopic of trust, \"Trust\" and \"Compliance\" were used as keywords. The other keywords selected were \"Cloud\" and \"Blockchain\". These keywords were then combined to specify the search. To ensure that only papers that focused on the selected keywords were identified, only the papers' titles and abstracts were searched. In total, 1495 peer-reviewed papers were found. After removing duplicates, 957 papers remained. Our focus was on reviewing high-quality scientific literature, as is generally recommended in research [17]. Therefore, we focused on peer-reviewed articles. Finally, the text corpus consisted of the titles and abstracts of 957 papers. In the next step, this corpus was prepared for further text analysis. For this purpose, the Natural Language Toolkit (NLTK) developed in Python was used [18]. Using the NLTK, first, the stop words were removed from the text corpus, and then lemmatization and stemming (snowball algorithm) were applied to bring the content of the text corpus into its root forms [14]. The processed text corpus was then analyzed using linear discriminant analysis (LDA) [19]. Nine trending topics (shown in Figure 1) related to the search strings could be identified. Note that LDA is not a strict clustering algorithm; thus, the same words might be used in multiple topics [19]. The statistical word distribution of each identified topic is shown in Figure 2. The number of identified words in each trending topic is nearly normally distributed. Hence, the authors accepted the LDA-created topics. The nine identified trending topic groups were named as follows: Topic 0: trading; Topic 1: vehicular; Topic 2: access management; Topic 3: payment; Topic 4: authentication; Topic 5: Internet of Things (IoT); Topic 6: cloud computing; Topic 7: legal and trust; and Topic 8: healthcare. Once the trends and their interrelationships were identified, this work was placed in the context of the trends. This work can be classified into Topic 6 and Topic 7. A total of 220 of the 957 identified papers were assigned here. In a final step, a literature review based on the work of [PERSON] et al. [20] was conducted, starting with the top 10 papers whose content most closely matched the keywords from Topics 6 and 7, which were identified and used in the initial literature for this article.