In recent decades, big data analysis has become the most important research topic. Hence, big data security offers Cloud application security and monitoring to host highly sensitive data to support Cloud platforms. However, the privacy and security of big data has become an emerging issue that restricts the organization to utilize Cloud services. The existing privacy preserving approaches showed several drawbacks such as a lack of data privacy and accurate data analysis, a lack of efficiency of performance, and completely rely on third party. In order to overcome such an issue, the Triple Data Encryption Standard (TDES) methodology is proposed to provide security for big data in the Cloud environment. The proposed TDES methodology provides a relatively simpler technique by increasing the sizes of keys in Data Encryption Standard (DES) to protect against attacks and defend the privacy of data. The experimental results showed that the proposed TDES method is effective in providing security and privacy to big healthcare data in the Cloud environment. The proposed TDES methodology showed less encryption and decryption time compared to the existing Intelligent Framework for Healthcare Data Security (IFHDS) method.

Key management shows an important role in the information transmissions. This paper analyzes the more improved and cost-effective key management scheme with respect to transmission overhead, computation cost. A key management policy is effectually employed for the purpose of generating a distinctive convention key for ample members to promise the security of their prospect communications, and this agreement can be allied in cloud computing for the purpose of assisting protected and skilled data distribution. The Key exchange protocols provides the two or more members to decide on a shared secret key and later be able to used for encrypting a long message. The complexity of Discrete Logarithm Problem (DLP) as basic problem considered in Diffie-Hellman Key Exchange Protocol. The Elliptic curve Diffie-Helman (ECDH) is considered as an expansion to the standard Diffie-Hellman. The ECDH function is ideal for a wide range of cryptographic functions. Key management based on ECDH provides much security on key meanwhile having more amount of storage cost and execution time involving in this. The ECDH Curve 25519 makes much faster in execution and lesser storage cost due its structure which uses much higher prime group of the order 2 ²⁵² .

The term cryptography which means “secret writing” has become the foundation primitive in providing security for many communication applications. In many applications, particularly in Group Communication, there is a need to hide secret data like passwords, encryption key, recipes etc. Here, efficient key management protocols are required to provide security for group's secret data, because it is very challenging to provide security especially when group of more members and those members are present in different locations with diverse mechanisms of protection. One among them is Secret Sharing Schemes (SSS) which allow to split and share the group's secret among group members. The secret can be retrived once, when some threshold member combines their share value in a defined way. Across the years, several schemes on Secret Sharing have been presented. In this view, this paper mainly focuses on three different Secret Sharing schemes, such as Shamir's Secret Sharing based on Polynomial Interpolation, Asmuth-Bloom Secret Sharing Scheme based on Chinese Remainder Theorem and Verifiable Secret Sharing Scheme based on Elliptic Curves. All these three schemes have been studied, algorithms are programmed using MATLAB 7.0 tool and executed. It has been observed that all the three schemes works satisfactorily according to the properties defined by the inventors of the schemes.

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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.

A wireless sensor network (WSN) is a collection of nodes fitted with small sensors and transceiver elements. Energy consumption, data loss, and transmission delays are the major drawback of creating mobile sinks. For instance, battery life and data latency might result in node isolation, which breaks the link between nodes in the network. These issues have been avoided by means of mobile data sinks, which move between nodes with connection issues. Therefore, energy aware multiobjective improved remora optimization algorithm and multiobjective ant colony optimization (EA-MIROA-MACO) is proposed in this research to improve the WSN’s energy efficiency by eliminating node isolation issue. MIRO is utilized to pick the optimal cluster heads (CHs), while multiobjective ant colony optimization (MACO) is employed to find the path through the CHs. The EA-MIROA-MACO aims to optimize energy consumption in nodes and enhance data transmission within a WSN. The analysis of EA-MIROA-MACO’s performance is conducted by considering the number of alive along with dead nodes, average residual energy, and network lifespan. The EA-MIROA-MACO is compared with traditional approaches such as mobile sink and fuzzy based relay node routing (MSFBRR) protocol as well as hybrid neural network (HNN). The EA-MIROA-MACO demonstrates a higher number of alive nodes, specifically 192, over the MSFBRR and HNN for 2,000 rounds.