Editorial for the Special Issue on Quality Assessment of Data Security

Abstract

Due to rapid technical advancements, many devices such as sensors, embedded systems, actuators, and mobile/smart devices receive huge amounts of information through data exchange and interconnectivity. From this increase in the exchange of data, there has also been a direct correlation to sensitive information that also moves through systems continuously. In this context, it is critical to ensure that both private and personal data is not disclosed and that any confidential information can be successfully hidden. Therefore, security and privacy have attracted a great deal of attention in academia and industry in recent decades. Not only is there a reason to protect against data leakage that is sensitive in nature, but it is also imperative to ensure that users of such systems trust the means by which their data is exchanged. Hundreds of security solutions have recently been discussed in the literature. However, the ability to properly manage the quality of security to ensure that developed models and algorithms can secure data is a very important task. To that end, only a limited number of works have addressed this problem directly. Since exchanged data usually is complex, researchers should also develop and investigate security models to perform quality assessments of data security. These tasks will ensure that threats from hackers or malware can be minimized. Security solutions can take on many forms. From cryptographic primitives all the way to machine learning and artificial intelligence, these potential fail-safes need to be properly researched, disseminated and discussed to ensure the next generation of systems will adhere to certain standards in the realm of security and privacy. This special issue saw a total of 21 submissions, from which five papers were published. It was intentional to adhere to a strict acceptance rate and ensure that only the best papers in the scope of the special issue were accepted. The following few paragraphs summarize the contributions that our special issue collection presents. In “A Survey on Edge Intelligence and Lightweight Machine Learning Support for Future Applications and Services,” Hoffpauir et al. provided a comprehensive survey of the emerging edge intelligence applications, lightweight machine learning algorithms, and their support for future applications and services. The survey started by analyzing the rise of cloud computing discussing its weak points, and identifying situations in which edge computing provides advantages over traditional cloud computing architectures. Then it dove into the survey the first section identifying opportunities and domains for edge computing growth, the second identifying algorithms and approaches that can be used to enhance edge intelligence implementations, and the third specifically analyzing situations in which edge intelligence can be enhanced using any of the aforementioned algorithms or approaches. In this third section, lightweight machine learning approaches are detailed. A more in-depth analysis and discussion of future developments follow. The