A Hybrid Secure Cloud Platform Maintenance Based on Improved Attribute-Based Encryption Strategies

In the modern era, Cloud Platforms are the most needed port to maintain documents remotely with proper security norms. The concept of cloud environments is similar to the network channel. Still, the Cloud is considered the refined form of network, in which the data can easily be stored into the server without any range restrictions. The data maintained into the remote server needs a high-security feature, and the processing power of data should be high to retrieve the data back from the respective server. In the past, there were several security schemes available to protect the remote cloud server reasonably. However, the attack possibilities over the cloud platform remain; only all the researchers continuously work on this platform without any delay. This paper introduces a hybrid data security scheme called the Improved Attribute-Based Encryption Scheme (IABES). This IABES combines two powerful data security algorithms: Advanced Encryption Standard (AES) and Attribute-Based Encryption (ABE) algorithm. These two algorithms are combined to provide massive support to the proposed approach of data maintenance over the remote cloud server with high-end security norms. This hybrid data security algorithm assures the data cannot be attacked over the server by the attacker or intruder in any case because of its robustness. The essential generation process generates a credential for the users. It cannot be identified or visible to anyone as well as the generated certificates cannot be extracted even if the corresponding user forgets the credentials. The only way to get back the certification is resetting the credential. The obtained results prove the accuracy level of the proposed cypher security schemes compared with the regular cloud security management scheme, and the proposed algorithm essential generation process is unique. No one can guess or acquire it. Even the person may be the service provider or server administrator. For all, the proposed system assures data maintenance over the cloud platform with a high level of security and robustness in Quality of Service

Editor’s Note

Artificial Intelligence (AI) represents one of the fastest growing areas of knowledge, sectors and fields of action globally. This growth has allowed to mark different positions, where the most favorable ones are oriented to its unquestionable contribution to facilitate decision making in various fields of society, as well as other sectors that mark a strong position for its use to be carried out in a regulated and measured way due to the scope and risks to which we are exposed. For this reason, rigorous methods are increasingly required for the design and development of AI-based computational models; methods that involve strict mechanisms for their validation, as well as the analysis of possible risks and scope that they may have on the field of application where they are being exposed. This type of aspects would definitely mark a valuable and relevant milestone to define several paths within which we can find two: 1) if it is definitely necessary to set limits on the use of AI by establishing increasingly sophisticated regulatory frameworks on various areas involving data protection and regulated use of the same, and 2) to remove all barriers so that it can be exploited openly in all its dimensions in any area of our society. Hence the importance of analysing the different risks and threats that AI may present within the particular context in which it is being applied. Based on this panorama, this regular edition of the “International Journal Interactive Multimedia and Artificial Intelligence” presents a series of papers where proposals are oriented to different fields and sectors, which make use of diverse approaches, methods, models and AI-based systems that allow us to have a generalized idea of how these challenges are being addressed in some fields of our society. In particular, this regular issue collects research topics focusing on addressing the problems of evolving recommender systems, classification models, decision support systems, system modelling, data analytics, optimization algorithms, image retrieval, deep neural networks, social network analysis, and the relevance of the design of User Experience (UX) proposals