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There are various system techniques or models which are used for access control by performing cryptographic operations and characterizing to provide an efficient cloud and in Internet of Things (IoT) access control. Particularly in cloud computing environment, there is a large-scale distribution of these traditional symmetric cryptographic techniques. These symmetric cryptographic techniques use the same key for encryption and decryption processes. However, during the execution of these phases, they are under the problems of key distribution and management. The purpose of this study is to provide efficient key management and key distribution in cloud computing environment.

This paper uses the Cipher text-Policy Attribute-Based Encryption (CP-ABE) technique with proper access control policy which is used to provide the data owner’s control and share the data through encryption process in Cloud and IoT environment. The data are shared with the the help of cloud storage, even in presence of authorized users. The main method used in this research is Enhanced CP-ABE Serialization (E-CP-ABES) approach.

The results are measured by means of encryption, completion and decryption time that showed better results when compared with the existing CP-ABE technique. The comparative analysis has showed that the proposed E-CP-ABES has obtained better results of 2373 ms for completion time for 256 key lengths, whereas the existing CP-ABE has obtained 3129 ms of completion time. In addition to this, the existing Advanced Encryption Standard (AES) scheme showed 3449 ms of completion time.

The proposed research work uses an E-CP-ABES access control technique that verifies the hidden attributes having a very sensitive dataset constraint and provides solution to the key management problem and access control mechanism existing in IOT and cloud computing environment. The novelty of the research is that the proposed E-CP-ABES incorporates extensible, partially hidden constraint policy by using a process known as serialization procedure and it serializes to a byte stream. Redundant residue number system is considered to remove errors that occur during the processing of bits or data obtained from the serialization. The data stream is recovered using the Deserialization process.

The purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.

This section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.

For health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.

The Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

In the cloud-computing environment, privacy preservation and enabling security to the cloud data is a crucial and demanding task. In both the commercial and academic world, the privacy of important and sensitive data needs to be safeguarded from unauthorized users to improve its security. Therefore, several key generations, encryption and decryption algorithms are developed for data privacy preservation in the cloud environment. Still, the outsourced data remains with the problems like minimum data security, time consumption and increased computational complexity. The purpose of this research study is to develop an effective cryptosystem algorithm to secure the outsourced data with minimum computational complexity.

A new cryptosystem algorithm is proposed in this paper to address the above-mentioned concerns. The introduced cryptosystem algorithm has combined the ElGamal algorithm and hyperchaotic sequence, which effectively encrypts the outsourced data and diminishes the computational complexity of the system.

In the resulting section, the proposed improved ElGamal cryptosystem (IEC) algorithm performance is validated using the performance metrics like encryption time, execution time, decryption time and key generation comparison time. The IEC algorithm approximately reduced 0.08–1.786 ms of encryption and decryption time compared to the existing model: secure data deletion and verification.

The IEC algorithm significantly enhances the data security in cloud environments by increasing the power of key pairs. In this manuscript, the conventional ElGamal algorithm is integrated with the pseudorandom sequences for a pseudorandom key generation for improving the outsourced cloud data security.