Enhancing Confidentiality and Privacy of Data in Motion from Embedded Systems

Data has become the new “oil” and is a drive for the knowledge based digital economy. Like any asset of a value, data has numerous security related challenges associated with its integrity, confidentiality and availability. Furthermore, emerging computing paradigms such as cloud and grid computing are enlarged the physical foot print of data in a given system, hence creating new challenges associated to data security. The new computing technologies such as wireless sensor networks and their applications in various fields present new challenges associated with Data Confidentiality and Privacy. Thus, this study aimed at improving confidentiality and Privacy of data in motion in wireless Vehicle Sensors.

The study used design science philosophy and applied inductive research strategy, since it aims at providing a solution to an issue in a unique domain whose problem and solution are entwined. To address objectives one, a mixed methods approach was used that involved both qualitative and quantitative methods of data collection and analysis. The design of the framework was modeled using a Unified Modeling Language. The research revealed that there is existence of data from the embedded systems used without clients’ consent. It was also revealed that enhancing confidentiality and privacy of data in motion in embedded systems save organizations from financial loss. The study identified several major weaknesses and vulnerabilities in existing frameworks, including lack of end user awareness, cyber threats, breaches in data in motion, gaps in data frameworks, human errors, limited knowledge and access to data frameworks, insecure file sharing, and potential malicious actions or third-party infiltration. Basing on the above findings, a framework to enhance confidentiality and privacy of data in motion was designed.

The framework proposed herein, creates a robust shield around data, elevating security standards and instilling trust in the system's ability to safeguard sensitive information. Through meticulous attention to authorization, consent management, integrity verification, and abuse detection, the framework stands poised to elevate data security standards and foster trust in the system's ability to safeguard sensitive information. The study findings suggest that the framework for enhancing confidentiality and privacy of data in motion is perceived as effective and efficient by the respondents. This positive feedback underscores the importance of using the ECPDM framework to safeguard embedded data in motion.