Abstract. User experience may vary from one user to another based on the design characteristics for processing a certain task. Our review of the literature revealed that there is a noticeable lack of studies on the effect of mobile or m-commerce design usability on customers' trust to perform certain activities. We investigated the effect of m-commerce design usability in terms of navigability, supportability, readability, creditability, and content relevant. A close-ended questionnaire was distributed to 20 customers who were asked to perform some tasks. The result showed that majority of the participants found m-commerce design usability to influence their trust of the app. The mentioned usability elements were found to positively influence customers' trust except for supportability. Our findings provide some insights to the m-commerce designers and developers and extend the current understanding about the potential of design usability in explaining users' usage behavior.

Distributed Data Mining (DDM) is vital in various applications for processing large volumes of data. The datasets are saved in the local databases and operated by local communities, but it provides the solution locally and globally. However, the datasets are stored in a distributed manner which affects the scalability and reliability issues. In addition, locally stored data is influenced by security and privacy challenges. In addition, the third party may access the DDM, which causes authorization issues. Therefore, the DDM process fuses sensor data from different sources to improve knowledge discovery. During this process, the DDM faces several issues such as security concerns, privacy restrictions, technical barriers, and trust issues. To address these issues, distributed data mining (DDM) should be improved to handle homogeneous and heterogeneous data. This work uses the privacy protection-based distributed clustering (PPDC) algorithm to handle the privacy and security challenges while analyzing the distributed data. The clustering algorithm generates the semi-trusted third parties to form the cluster, which protects the data from unauthorized users. The semi-trusted party protect the locally analyzed solution by creating the random vector-based trusted process. Further, the process uses the optimized deep learning approach and clustering to improve the heterogeneous data analysis. Then the effectiveness of the introduced PPDC method is compared with existing methods, and the PPDC algorithm ensures the 0.202 error rate, 0.95 % of accuracy and manages the data security.