The information obtained from external sources within the cloud and the resulting computations are not always reliable. This is attributed to the absence of tangible regulations and information management on the part of the information owners. Although numerous techniques for safeguarding and securing external information have been developed, security hazards in the cloud are still problematic. This could potentially pose a significant challenge to the effective adoption and utilization of cloud technology. In terms of performance, many of the existing solutions are affected by high computation costs, particularly in terms of auditing. In order to reduce the auditing expenses, this paper proposes a well-organised, lightweight system for safeguarding information through enhanced integrity checking. The proposed technique implements a cryptographic hash function with low-cost mathematic operations. In addition, this paper explores the role of a semi-trusted server with regard to smart device users. This facilitates the formal management of information prior to distribution through the IoT-cloud system. Essentially, this facilitates the validation of the information stored and exchanged in this environment. The results obtained show that the proposed system is lightweight and offers features such as a safeguarding capability, key management, privacy, decreased costs, sufficient security for smart device users, one-time key provision, and high degree of accuracy. In addition, the proposed method exhibits lower computation complexity and storage expenses compared with those of other techniques such as bilinear map-based systems.

Decision-making plays an essential role in the management and may represent the most important component in the planning process. Employee attrition is considered a well-known problem that needs the right decisions from the administration to preserve high qualified employees. Interestingly, artificial intelligence is utilized extensively as an efficient tool for predicting such a problem. The proposed work utilizes the deep learning technique along with some preprocessing steps to improve the prediction of employee attrition. Several factors lead to employee attrition. Such factors are analyzed to reveal their intercorrelation and to demonstrate the dominant ones. Our work was tested using the imbalanced dataset of IBM analytics, which contains 35 features for 1470 employees. To get realistic results, we derived a balanced version from the original one. Finally, cross-validation is implemented to evaluate our work precisely. Extensive experiments have been conducted to show the practical value of our work. The prediction accuracy using the original dataset is about 91%, whereas it is about 94% using a synthetic dataset.

The cloud healthcare system has become the essential online service during the COVID-19 pandemic. In this type of system, the authorized user may login to a distant server to acquire the service and resources they demand, we need full security procedures that cover criteria such as authentication, privacy, integrity, and availability. The journey of security for any healthcare system starts with the authentication of users based on their privileges. Traditional user authentication mechanisms, such as password and personal identification number (PIN) typing, are vulnerable to malicious attacks like on/offline, insider, replay, guessing, and shoulder surfing. To address these issues, we proposed a secure authentication scheme that uses the authenticated delegating mechanism based on two factors: a one-time password and generating a secure variable vector from a legible user's digital image to enable the permission of a user through the back-end database of a cloud server. The proposed mutual authentication can protect the information against well-known attacks, ensure the user's privacy, and key management. Moreover, comparisons with existing schemes show that the proposed scheme supplies more privacy, security metrics, and resistance to attacks than the others while being more efficient in computation and communication costs.

The cloud healthcare system has become the essential online service during the COVID-19 pandemic. In this type of system, the authorized user may login to a distant server to acquire the service and resources they demand, we need full security procedures that cover criteria such as authentication, privacy, integrity, and availability. The journey of security for any healthcare system starts with the authentication of users based on their privileges. Traditional user authentication mechanisms, such as password and personal identification number (PIN) typing, are vulnerable to malicious attacks like on/offline, insider, replay, guessing, and shoulder surfing. To address these issues, we proposed a secure authentication scheme that uses the authenticated delegating mechanism based on two factors: a one-time password and generating a secure variable vector from a legible user's digital image to enable the permission of a user through the back-end database of a cloud server. The proposed mutual authentication can protect the information against well-known attacks, ensure the user's privacy, and key management. Moreover, comparisons with existing schemes show that the proposed scheme supplies more privacy, security metrics, and resistance to attacks than the others while being more efficient in computation and communication costs.