Optimal Multikey Homomorphic Encryption with Steganography Approach for Multimedia Security in Internet of Everything Environment

: Recent developments of semiconductor and communication technologies have resulted in the interconnection of numerous devices in offering seamless communication and services, which is termed as Internet of Everything (IoE). It is a subset of Internet of Things (IoT) which ﬁnds helpful in several applications namely smart city, smart home, precise agriculture, healthcare, logistics, etc. Despite the beneﬁts of IoE, it is limited to processing and storage abilities, resulting in the degradation of device safety, privacy, and efﬁciency. Security and privacy become major concerns in the transmission of multimedia data over the IoE network. Encryption and image steganography is considered effective solutions to accomplish secure data transmission in the IoE environment. For resolving the limitations of the existing works, this article proposes an optimal multikey homomorphic encryption with steganography approach for multimedia security (OMKHES-MS) technique in the IoE environment. Primarily, singular value decomposition (SVD) model is applied for the separation of cover images into RGB elements. Besides, optimum pixel selection process is carried out using coyote optimization algorithm (COA). At the same time, the encryption of secret images is performed using poor and rich optimization (PRO) with multikey homomorphic encryption (MKHE) technique. Finally, the cipher image is embedded into the chosen pixel values of the cover image to generate stego image. For assessing the better outcomes of the OMKHES-MS model, a wide range of experiments were carried out. The extensive comparative analysis reported the supremacy of the proposed model over the rennet approaches interms of different measures.


Introduction
Internet of Everything (IoE) represents a fantastic vision in the future, where everything is interconnected to the Internet, thus facilitating decision-making and offering intelligent service. IoE application based on interdisciplinary technological innovation includes low power communications, big data analytics, sensor, and embedded techniques [1]. Over the years, increasing new technologies are emerged to provide new bricks to construct IoE. Firstly, the advancement in sensor and embedded techniques have made the Internet of [1]. Over the years, increasing new technologies are emerged to provide new bricks to construct IoE. Firstly, the advancement in sensor and embedded techniques have made the Internet of Things (IoT) node being less energy consumption and more portable [2]. The IoT involves the interconnectivity of physical objects and data input and output, whereas the IoE is a comprehensive term that refers to the interconnectivity of various technologies, processes, and people. Next, the presence of Lower Power Wide Area Network (LPWAN) technology empowers the ubiquitous network connection of lower power IoT nodes [3]. Further, the availability of massive IoT data and the innovation in artificial intelligence have driven the intelligence of IoE. Thus, IoE is employed in wide-ranging applications like intelligent transportation systems, smart manufacturing, and smart agriculture. Figure 1 depicts the process of IoE. Many possible security risks are experienced in IoE, attributed to the vulnerability of transmission protocol and resource limitation of IoE node [4]. Especially, the existing IoE mainly adopts the lower-cost and simplified access protocols for reducing cost of the network whereas it makes the communication vulnerable to malicious attacks like forging and eavesdropping. On the contrary, the data emitted from end node is eavesdropped (or wiretapped) by malicious node; at the same time, and pseudo-base-station could easily forge the standard IoE transmission connections to attain IoE data [5]. Thus, an efficient and easy-deployed security method is needed for protecting IoE communication from malicious attacks. Typically, encryption is utilized for ensuring data privacy and confidentiality, at the expense of utility (for example, searching on ciphertext becomes challenging and costly). Generally, Current encryption solution assumes that the cloud server is honest-but-curious, that implements search operation according to the agreed protocol however it might be interested in potentially learning sensitive data [6]. But in a real-time scenario the cloud server is highly possible to be semi-trusted in the sense that it never accessed or might delete rarely information to conduct partial search operation, output a fraction of inaccurate search results, cut costs (for example, minimizing computation overhead and storage space), etc. [7].
Steganography represents the fabrication technique that is utilized as digital information to the cover media. It provides security to the hidden and sensitive data existing in the image that is undetectable by human vision [8]. Still, several studies in this field are Many possible security risks are experienced in IoE, attributed to the vulnerability of transmission protocol and resource limitation of IoE node [4]. Especially, the existing IoE mainly adopts the lower-cost and simplified access protocols for reducing cost of the network whereas it makes the communication vulnerable to malicious attacks like forging and eavesdropping. On the contrary, the data emitted from end node is eavesdropped (or wiretapped) by malicious node; at the same time, and pseudo-base-station could easily forge the standard IoE transmission connections to attain IoE data [5]. Thus, an efficient and easy-deployed security method is needed for protecting IoE communication from malicious attacks. Typically, encryption is utilized for ensuring data privacy and confidentiality, at the expense of utility (for example, searching on ciphertext becomes challenging and costly). Generally, Current encryption solution assumes that the cloud server is honest-but-curious, that implements search operation according to the agreed protocol however it might be interested in potentially learning sensitive data [6]. But in a real-time scenario the cloud server is highly possible to be semi-trusted in the sense that it never accessed or might delete rarely information to conduct partial search operation, output a fraction of inaccurate search results, cut costs (for example, minimizing computation overhead and storage space), etc. [7].
Steganography represents the fabrication technique that is utilized as digital information to the cover media. It provides security to the hidden and sensitive data existing in the image that is undetectable by human vision [8]. Still, several studies in this field are needed for selecting appropriate tradeoffs among the performance evaluations like payload capacity, security, and imperceptibility. Now, secure information is transmitted through text messages, images, audio, and videos files. To transmit this message in a hidden method, there is necessity for steganography. In this approach, the embedded secret message in the file is transported to the user alternatively, whereby the message is unconcealed [9]. Even though there is much software accessible online for data security, there is another software that is in usage by hackers for decrypting the hidden information [10].
This article proposes an optimal multikey homomorphic encryption with steganography approach for multimedia security (OMKHES-MS) technique in the IoE environment. The OMKHES-MS model initially uses singular value decomposition (SVD) model. In addition, the optimal pixel points in the cover image are chosen by the use of coyote optimization algorithm (COA). Moreover, the encryption of secret images is performed using poor and rich optimization (PRO) with multikey homomorphic encryption techniques. Furthermore, the cipher image is embedded into the chosen pixel values of the cover image to generate stego image. To investigate the enhanced performance of the OMKHES-MS model, a comprehensive simulation analysis is performed and the results are investigated under several aspects.

Related Works
In [11], a machine learning (ML) based structure was presented for identifying benign and malicious nodes from an IoE network functioning with big data. A new technique to co-operate of extreme gradient boosting (XGBoost) and deep learning (DL) techniques together with genetic particle swarm optimization (GPSO) technique for discovering the optimum structures of individual ML techniques are presented. But the simulation, it can be demonstrated that GPSO based learning techniques offer reliable, robust, and scalable solutions. The authors in [12] analyzed the existing structures utilized to develop secure IoE with big data analytics. Big data is a group of data created in the sensor embedding from nearby physical objects. This data was utilized to analyze surrounding and development depending upon the inference. IoE utilizes this information to automation of electronic equipment from the surrounding environments.
Mohanty et al. [13] presented a novel blockchain structure named as PUFchain and presents a novel consensus technique named "Proof of physical unclonable functions (PUF)-Enabled Authentication" (PoP) to be utilized from PUFchain. The presented PoP is the PUF combined as to before presented Proof-of-Authentication (PoAh) consensus technique is named as "HardwareAssisted PoAh (HA-PoAh)". Miao et al. [14] introduced a fair and dynamic data sharing framework (FairDynDSF) from the multi-owner setting. Utilizing FairDynDSF, one is to verify the correctness of searching outcomes, dynamic update, attain fair arbitration, and multi-keyword search.
Singh et al. [15] proposed a secure structure Blockchain and Fog-based Architecture Network (BFAN) for IoE application from the smart city. The presented infrastructure secures sensitive information with Blockchain, encryption, and authentication. It supports the System-developer and Architect for deploying the application from smart city paradigm. The purpose of presented infrastructure is for reducing the latency and energy, and make sure enhanced security features with Blockchain technologies. Li et al. [16] presented a privacy-enhanced federated learning model for IoE. 2 processes that were executed in our systems such as local adaptive differential privacy (LADP) and randomized response (RR) processes. The RR was implemented for preventing the server from discovering if upgrades are gathered from all the rounds.

The Proposed Model
In this study, a new OMKHES-MS technique has been developed to accomplish security and privacy in the IoE environment. The OMKHES-MS model involves a series of processes namely SVD, COA based optimum pixel selection, MKHE based encryption, and PRO based key generation. Finally, the cipher image from the MKHE technique is embedded into the chosen pixel values of the cover image to generate stego image. The design of PRO algorithm helps in choosing optimum keys for the generation of cipher images. Figure 2 demonstrates the overall process of OMKHES-MS technique.  Figure 2 demonstrates the overall process of OMKHES-MS technique.

Singular Value Decomposition
SVD is an influential tool that takes several applications like pattern detection and data compression. SVD allows robust and reliable matrix factorizes for extracting the algebraic and geometric invariant features of images. The SVD factorization a square/nonsquare matrices as to 2 orthogonal matrices and singular value (SV) matrix. The spatial domain feature of images of the size 100 × 100 is demonstrated utilizing SVD by feature vector group of size 1 × 100. It can be predictable for speeding up the computational procedure by removing irrelevant features but preserving as a lot of data as feasible from the images. The SVD of rectangular real complex matrix has been formulated as follows [17].
(3) ≥ ≥ … … . ≥ . (4) where A refers to the × matrices, implies the × orthonormal matrices, V signifies the × orthonormal matrices, and defines the diagonal matrices of sizes × that is collected of SVs of A such that it holds non-negative number.
The diagonal entry of Σ matrix signifies the SV and it can be superior values related to the entries of and V so that matrix of size × is decreased to vector of sizes .

Singular Value Decomposition
SVD is an influential tool that takes several applications like pattern detection and data compression. SVD allows robust and reliable matrix factorizes for extracting the algebraic and geometric invariant features of images. The SVD factorization a square/non-square matrices as to 2 orthogonal matrices and singular value (SV) matrix. The spatial domain feature of images of the size 100 × 100 is demonstrated utilizing SVD by feature vector group of size 1× 100. It can be predictable for speeding up the computational procedure by removing irrelevant features but preserving as a lot of data as feasible from the images. The SVD of rectangular real complex matrix A has been formulated as follows [17]. where where A refers to the m × n matrices, U implies the m × m orthonormal matrices, V signifies the n × n orthonormal matrices, and Σ defines the diagonal matrices of sizes m × n that is collected of SVs of A such that it holds non-negative number.
The diagonal entry of Σ matrix signifies the SV and it can be superior values related to the entries of U and V so that matrix of size m × n is decreased to vector of sizes n. The SV is ranked in descending order; a primary entry of SV matrix contains the best substantial data but the final entry at the vector comprises the least significant data. The SV has the energy data but the orthogonal matrices contain the essential data. U T and V T are the transpose of matrices U and V correspondingly. I demonstrates the identity matrix. The column of U is termed as left singular vector of A whereas the column of V is named as the right singular vector of A.

Optimal Pixel Selection Using COA
For selecting the pixel points in the cover image optimally, the COA is applied. A current metaheuristic method for global optimization named COA is adapted. The main concept is depending on canis latrans species that mostly exist in North America. The process is adopted for considering the social organization of the agent named coyotes that serves as an algorithmic construction. The social behavior of coyote serves as a design variable and it is determined in the following [18]: This social condition includes adopting the coyote to the environment name f it p,t c ∈ . The adaptation of the coyotes to the present social situation was evaluated by: Initially, the coyotes are arbitrarily allocated to the packs though the detail that they might leave sometimes their packs and developed solitary or affiliated to other packs. The coyote's transfer among packs increases the interaction of population with their culture. An alpha is selected from the three COA alphas: With respect to the COA, it is assumed that each coyote has been ordered for exchanging the social culture. Thus, the present data of coyotes are interrelated and estimated as follows.
The social condition ranking of each coyote at t instant is offered by variable O p,t . In another word, the median social condition of each coyote from that certain pack is applied for determining the cultural tendency. The birth of coyote represented as age p,t c , is a function of social grouping of two parents who are selected arbitrarily with regards to the effect of environment. Figure 3 demonstrates the flowchart of COA.
where soc p,t r1,j and soc p,t r2,j denotes the social condition of two coyotes r 1 and r 2 are randomly chosen and include the pth pack at time t. j1 and j2 denote the dimension of optimization problem that is chosen arbitrarily. P s and P a represents the possibility of scattering and the possibility of association, correspondingly. R j is an arbitrarily created value in the range of variable bound. The cultural diversity of coyotes from the pack is performed by the two probabilities P s and P a , in the following: where D show the problem dimension inside the pack. The coyote under alpha effect δ 1 and pack effect δ 2 , is defined in the following: and pack effect , is defined in the following: The alpha and pack effect remains significant parameter in the update of social coyote condition as follows: , , = , , The update procedure of the social condition can be accomplished by considering the subsequent condition:  The alpha and pack effect remains significant parameter in the update of social coyote condition as follows: soc The update procedure of the social condition can be accomplished by considering the subsequent condition:

Secret Image Encryption Using MKHE Technique
In order to effectually encrypt the secret image before steganography, the MKHE technique is applied. An MKHE is a cryptosystem that permits us for evaluating an arithmetic circuit on cipher-text, maybe encrypting in several keys. Assume that M remains the message space with arithmetic infrastructure [19]. An MKHE method has 5 PPT techniques (Eval, Setup, Enc, KeyGen, and Dec). It is considered that all contributing parties have a reference (index) to their public and confidential keys. A multi-key cipher-text implicitly has an arranged set T = {id 1 , . . . , id k } of connected references. For instance, a fresh cipher-text ct ← MKHE. Enc(µ; pk id ) equals to single-element set T = {id} but the size of references fixed obtains superior to the computation amongst cipher-text in various party developments.

•
Setup: pp ← MKHE. Setup 1 λ . Proceeds the secured parameter as input and returned the public parameterization. It can be considered that every other technique implicitly gets pp as input.
To provide a cipher-text ct with equivalent order of confidential keys, outcomes a plaintext µ.
The equality is replaced by estimated equality same as the Cheon, Kim, Kim and Song (CKKS) technique to estimate arithmetic [16].

Key Generation Using PRO Algorithm
For optimal key generation process, the PRO algorithm is applied, which depends on people's wealth behavior in the society. Generally, it is classified into two financial groups within a society. Initially, it comprises of wealthier person (wealth is greater when compared to average). Second, it comprises poor people (wealth is lesser when compared to average). Rich economical class people attempt to extend the class gap by observing them from poor economical groups. During the optimization problem, every solution from the Poor population moves towards the global optimum solution in the searching space by learning from the rich solution from the rich population [20].

Initial population
The set of solutions from the present generation is named a population. The candidate solution in the population consists of the rich and poor economical solution or person. Consider 'N' is the population size. We arbitrarily create 'N' solutions with arbitrary numbers within [0, 1]. Next, the digitization procedure is employed to every location of solution to convert real value to binary values as follows where rand represents an arbitrary value among 0 and 1. The candidate solution in the population is ordered according to the objective function. The topmost part of the population is denoted as rich economic class of people and the bottom part of the population is represented as poor economic class of people.
Fitness Function The COA intends to derive an objective function depending upon the fitness function. The major intention of the COA is to design a novel image steganography technique with the minimization of error (MSE) and maximization of PNSR. It is evaluated as The desired minimized and maximized value is obtained by the use of the inspired Whale optimization technique.

Generating new solutions
The rich people move towards to rise the economic class gap by observing them from the poor economical group. The poor economical class people move towards to decrease the economic class gap by learning from the rich economical class for increasing the financial condition. The general behavior of rich and poor people can be utilized for generating a new solution.
χ new poor,i,j = χ old poor,i,j + α * χ 0 rich,best,j + χ 0 rich,mean,j + χ 0 rich,worst,j To improve the level of security, the optimal keys of the MKHE technique are generated by the use of PRO algorithm.  Figure 4 shows the sample set of benchmark images used as secret images and the respective image histograms are offered in Figure 5. Appl. Sci. 2022, 12, x FOR PEER REVIEW 9 Figure 4 shows the sample set of benchmark images used as secret images and respective image histograms are offered in Figure 5.               A comparative SC analysis of the OMKHES-MS model with existing techniques under distinct test images is performed in Figure 8    A comparative SC analysis of the OMKHES-MS model with existing techniques under distinct test images is performed in Figure 8.  A comprehensive AD and MD assessment of the OMKHES-MS model is compared with recent methods in Table 3. The experimental values notified that the OMKHES-MS model has gained effectual outcome with maximum values of AD and MD.  A comprehensive AD and MD assessment of the OMKHES-MS model is compared with recent methods in Table 3. The experimental values notified that the OMKHES-MS model has gained effectual outcome with maximum values of AD and MD.           A comprehensive NCC and NAE assessment of the OMKHES-MS model is compared with recent methods in Table 4. The experimental values notified that the OMKHES-MS model has gained effectual outcome with maximum values of NCC and NAE. A broad NCC assessment of the OMKHES-MS model with compared methods under several test images is provided in Figure 12. The figure portrayed that the OMKHES-MS model has resulted in better outcomes with improved NCC values under every image.      From the detailed results and discussion, it can be clear that the OMKHES-MS model has reached superior results over the other methods interms of different measures. Therefore, the OMKHES-MS model can be utilized as an effective tool for accomplishing multimedia security in the IoE environment.

Conclusions
In this study, a new OMKHES-MS technique has been developed to accomplish security and privacy in the IoE environment. The OMKHES-MS model involves a series of processes namely SVD, COA based optimum pixel selection, MKHE based encryption, and PRO based key generation. Finally, the cipher image from the MKHE technique is embedded into the chosen pixel values of the cover image to generate stego image. The design of PRO algorithm helps in choosing optimum keys for the generation of cipher images. To investigate the enhanced performance of the OMKHES-MS model, a comprehensive simulation analysis is performed and the results are investigated under several aspects. The extensive comparative analysis reported the supremacy of the proposed model over the rennet approaches interms of different measures. Therefore, the OMKHES-MS model can be utilized as an effective tool to accomplish security in the IoE environment. In future, lightweight crytographic techniques can be designed for IoE environment. Besides, detailed security analysis of the OMKHES-MS model will be made in our future work. Data Availability Statement: Data sharing not applicable to this article as no datasets were generated during the current study.