1. Introduction

2. Literature Review

3. Proposed Method

Figure 3. The main architecture of the data security and privacy model.

Figure 3. The main architecture of the data security and privacy model.

3.1. Graph Convolutional Network Clustering (GCNC)

The best of both worlds is achieved by Graph convolutional network clustering technique, this has the same memory complexity as conventional SGD and full gradient descent have the same temporal complexity per epoch. Consider the scenario in which each batch's embeddings are determined for a collection of nodes B from layer 1 to layer L. Given that every layer of computation uses the same subgraph $A_{B,B}\left(LinkswithiB\right)$we may assume that the amount of edges in this batch,${‖A_{B,B}‖}_0$. To indicates the embedding utilisation. we need build a batch B that maximises within-batch edges in order to increase embedding utilisation. In order to link the effectiveness of SGD updates with graph clustering techniques, Figure 1 shows the complete graph G and the graph with the clustering partition G as examples of the community expansion. Cluster-GCN, as can be shown, can concentrate on the neighbours within each cluster rather than conducting a thorough neighbourhood search.

Figure 4. The distinction between our suggested cluster technique and conventional graph convolution is the neighbourhood expansion. The extension of neighbourhood nodes begins at the red node. Reflects the growing convolution suffers from hyperbolic neighbourhood growth, however our technique can stop expensive neighbourhood expansion.

Figure 4. The distinction between our suggested cluster technique and conventional graph convolution is the neighbourhood expansion. The extension of neighbourhood nodes begins at the red node. Reflects the growing convolution suffers from hyperbolic neighbourhood growth, however our technique can stop expensive neighbourhood expansion.

For a graph $G=(V,\epsilon ,A$) which consists of N=$\left|V\right|$ vertices and $\left|\epsilon \right|$edges such that an edge among any two vertices$i$ and $j$ represents their similarity. The corresponding adjacency matrix A is an $N\times N$ sparse matrix with $(i,j)$ entry equaling to 1 if there is an edge between $i$ and $j$ and 0 otherwise. G's partition its nodes into c groups: $V=[V_1,\dots V_c],$ where $V_t$ represents consists of the nodes in the t-th partition. L is number of layers, F is number of features, N is number of nodes, b is the batch size. As a result, we have c sub graphs

$$\overline{G}=\left[G_1,\dots G_c\right]=\left[\left\{v_1,{\epsilon }_1\right\},\dots ,\left\{v_c,{\epsilon }_c\right\}\right]$$

(1)

where each ${\epsilon }_t$ is made up only of the connections connecting the nodes in $v_t$. The adjacency matrix is divided into c² submatrices after nodes are reorganised as

$$A=\overline{A}+∆=\left[\begin{array}{ccc}{A}_{11}& 1& A_{1c}\\ 1& 0& 1\\ A_{c1}& 1& A_{cc}\end{array}\right]$$

(2)

And

$$\overline{A}=\left[\begin{array}{ccc}{A}_{11}& 1& 0\\ 1& 0& 1\\ 0& 1& A_{cc}\end{array}\right],∆=\left[\begin{array}{ccc}0& 1& A_{1c}\\ 1& 0& 1\\ A_{c1}& 1& 0\end{array}\right]$$

(3)

The links within $G_t$ are contained in each diagonal block $A_{tt}$ is a $\left|v_t\right|\times \left|v_t\right|$ adjacency matrix. The adjacency matrix for the graph $G_t$. $\overline{A}$ is the adjacency matrix for graph $\overline{G}$; $A_{st}$ contains the connections between its two partitions $v_t$ ;$∆$. Similar to this, we can divide the $\widehat{X}$and $Y$ into $[V_1,\dots V_c]$ as $[X_1,\dots X_c]$ according to the partition [$Y_1,\dots Y_c$ ] where $X_t$ and $Y_t$are made up.

The calculation $\overline{G}$ is that it allows for the decomposition of the objective function of GCN into many groups (clusters). The final embedding matrix is if we use $\overline{A^{\prime }}$ to represent the normalised version of $\overline{A}$.

$$Z^{\left(L\right)}=\overline{A^{\prime }}\sigma \left(\overline{A^{\prime }}\sigma \left(\dots \sigma \left(\overline{A^{\prime }}X{W}^{\left(0\right)}\right)W^{\left(1\right)}\right)\dots \right)W^{\left(L-1\right)}$$

(4)

Significantly less difficult to construct than the neighbourhood search method utilised in earlier SGD-based training methods. Because$\overline{A}$ has a block-diagonal structure (remember that ${\overline{A}}_{tt}^{\prime }$ is the matching diagonal block of $\overline{A^{\prime }}$). Additionally, it has broken down into.

$$L_{\overline{A^{\prime }}}={\sum }_t\frac{\left|v_t\right|}{N}{L}_{{\overline{A}}_{tt}^{\prime }}\mathrm{and}{L}_{{\overline{A}}_{tt}^{\prime }}=\frac{1}{\left|v_t\right|}\sum _{i\in v_t}loss(yi,z_i^{\left(L\right)})$$

(5)

The decomposition form in (3) and (4) serves as the basis for the Cluster-GCN (5). At each step, we sample a cluster $V_t$ and the conduct SGD to update based on the gradient of $L_{{\overline{A}}_{tt}^{\prime }}$ and this only requires the sub graph $A_{tt}$, the $X_t$, the $Y_t$ on the current batch models ${\left\{w^l\right\}}_{l=1}^L$Compared to the neighbourhood search strategy employed in earlier SGD-based training methods, the implementation merely calls for matrix components' forward and retrograde transmission (one block of (5)). As previously mentioned, the embedding utilisation is similar to the within-cluster linkages for each, therefore these are exactly what we require batch.1)Assuming that each node and its neighbours are often found in the same cluster, neighbourhood nodes have a high probability of remaining in the same cluster after a few hops. 2) We need to design a partition to reduce the amount of between-cluster links since we replace A with its block diagonal approximation A and the error is proportional to those links.

Each node in $v_t$ of$A_{tt}$ because each node only links to other nodes inside of $v_t$. Each batch's computation will only involve matrix products ${\overline{A}}_{tt}^{\prime }{X}_t^{\left(l\right)}{w}^{\left(l\right)}$and a few element-wise operations, thus the total computation time is$O({‖A_{tt}‖}_{0}F+b{F}^2)$. As a result, $O({‖A‖}_{0}F+N{F}^2)$ is the overall temporal complexity per epoch. On average, each batch only needs to compute $O\left(bL\right)$ embeddings, which is exponential to L rather than linear. For embedding storage, we simply must load b examples per batch and per layer's extracted features are saved using O(bLF) memory.

3.2. Magnify Pinpointing-Based Encryption(MPBE)

Only key creation, encryption, and decryption times are calculated in the suggested method. Currently, network overhead is a problem. This framework shows how user 1 and user 2 can access each other's data by using magnify Pinpointing-based encryption as a security, authentication, and authorization tool. Both user 1 and user 2 two users who access the cloud's services, is shown in Figure 5 as part of the cloud environment using the cloud database for access.

Figure 5. Framework for proposed work.

Figure 5. Framework for proposed work.

1. Private key generator (PKG) starts the setup process and decides the security parameters like the level of bits and type of curves. 2. Bob obtains the master public key from PKG. 3. Bob authenticates himself by issuing his identity to PKG and receives the private key for encrypting the data. 4. Similarly, Alice obtains the master public key from PKG. 5. Alice authenticates herself by issuing his identity to PKG and receives the private key for encrypting the data. 6. Bob sends his identity to Alice for generating the public key related to Bob’s identity. Alice will use this key to decrypt the data authenticated by Bob. 7. Alice gets the Bob’s data from database and decrypts it for accessing.

Role-based classification is faster with identity-based encryption. With these techniques, users are given access to the data after authenticating their identities. This plan is initially put into practise in proxy servers to remove unwanted users. The identities of allowed users are recorded in proxy servers, and whenever an unauthorised user tries to access the server's service, their access is revoked because no key matches that identity. As a result, in order to access the service, each user must register their identification. Security is becoming increasingly more in demand as a result of technological advancements like cloud computing. This system is used by several security companies to protect user data and information. Uploading and downloading of data or files is a crucial component of cloud security implementation. The effectiveness of an IBE scheme is also significantly influenced by network congestion and service speed. However, in order to improve time efficiency, the suggested method described in Section 3.2 focuses primarily on key creation, encryption, and decryption. The only times that are calculated in the proposed method are those for key generation, encryption, and decryption. At this time, network overhead is compromised. Using IBE as a security, authentication, and authorization tool, this system illustrates how Bob and Alice can access each other's data An authorised user's identity cannot be obtained by an unauthorised user because PKG stores user identities in Lagrange polynomial equation form, which is computationally challenging for an outsider to decipher and obtain the user's original identity. Because they communicate these identities using secure socket connections (SSL), when Bob sends his identification to Alice in the middle of the network, an attacker cannot obtain the identity. Hash-based key generation techniques are computationally costly, hence pairing-based key generation techniques are used instead.

Security is becoming increasingly more in demand as a result of technological advancements like cloud computing. This system is used by several security companies to protect user data and information. Uploading and downloading of data or files is a crucial component of cloud security implementation.

Let $G$ stand for a collection of prime order $p$. Group $G$ forms an accurate bilinear map onto Group $G_1$. $G_1$'s bilinear map representation is given by the formula $e$: $G\times G_1\to G_2$, where $g$ is the group $G$'s generator. The group size is determined by a security parameter, and each identity is represented by four strings, each of length $n.4$.

$$SL=\left({sl}_1,{sl}_2,{sl}_3,,,,{sl}_n\right).$$

(7)

From the SL bit strings length, utilised to produce fixed length n bit strings. The suggested Magnify Pinpointing-based encryption algorithm includes the following phases.

3.2.1. Initial Phase

Create the system parameters first. From $Z_p$ a underground is chosen at random. Pick an accidental maker $g$ from the set $G$ such that $g\in G$, fix the value $gI=g^{\alpha }$, and randomly choose $g_2$from the set $G$. Select a chance quantity $u$ such that $u^{\prime }\in G$ and a random $n$-length vector such that $U=\left\{u_i\right\}$ fuig after selecting all authority parameters. Finally, the public parameters $g,g1,g2,u\prime ,$ and $U$ are broadcast along with the master key $g_2^{\alpha }$.

3.2.2. Generation Phase

Let $v$be the $n$-bit string SL for the user, and let $V\subseteq \left\{1,\dots n\right\}$be the collection of all $i$ for which $v_i=1$. $V$ is split into two different, namely $V=\left\{v_1,v_2,\dots v_m\right\}$ and $\left\{V_{r_1},V_{r_2},\dots v_{r_m}\right\}$ such that $m+r_m=n$, where $V_{r_1}$ stands for a random value that is introduced to the suggested approach to increase security. The private key corresponding to identity $v$ is obtained by selecting a random value, as indicated in Equation (8).$u^{\prime }\prod _{i\in V}{v}_i$ is the Formula for group operation during the key generation

$$d_v=\left(u^{\prime }\prod _{i\in V}{v}_i\right)$$

(8)

$U=\left\{u_1,u_2|\dots u_n\right\}$ and V={$v_1,v_2\dots v_m$}such that $m<n$. Now, create a polynomial function using the Lagrange coefficient method and perform polynomial interpolation. We can conceal of v that can be effectively reconstructed from the available data points with the aid of polynomial interpolation. For the suggested strategy, the polynomial equation and Lagrange coefficient is

Lagrange coefficient is

$${∆}_{i,v}\left(x\right)=\sum _{i=0,k\in V}^n\left(\prod _{0<i<n,j\ne i}\frac{x-x_j}{x_i-x_j}\right)y_k$$

(9)

Where $x=u_{i}andy=v_k.$ Every user identity's random set $u_i$ is generated once, and each identity's Lagrange coefficient is generated using the same $u_i$ value. M-terms of identity will be used by authority value as a result of which a challenger will never learn the authentic user's original identity. As a result, it will be challenging to retrieve or deduce the key created for a specific SL. What if every user identity and U value is identical? The challenger will be unable to infer anything from the key in such case since ${∆}_{i,v}\left(x\right)$ produces an error of zero. This situation is unique. The greatest error between any two subsequent nodes will be shown, and the error created will be zero.

3.2.3. Encryption Phase

Let message $M(M\in G_1)$ and "c" be a random number selected from$Z_p$. Equation can be used for encryption for some identities $v$. (10). Encryption using three-key TDEA is prohibited unless otherwise approved by another NIST guidance.

$$=\left({e(g_1,g_2)}^{c}M,g^c,{\left(u^{\prime }\prod _{i=V}{V}_i\right)}^c\right)$$

(10)

3.2.4. Decryption Phase

Let $C=(C_1,C_2,C_3)$ be a valid cypher text under user identity v for message M. Then, using cypher text C as a key, $d_v=(d_1,d_2)$ as given in Equation (11)-(13).

$$C_1\frac{e(d_2,C_3)}{e(d_1,C_2)}={\left(e\right(g_1,g_2)}^{c}M)\frac{e\left(g^r,{\left(u^{\prime }\prod _{i=v}{v}_i\right)}^c\right)}{e\left(g_2^{\alpha },{\left(u,\prod _{i=v}{v}_i\right)}^r,g^c\right)},$$

(11)

$$=\left({e(g_1,g_2)}^{c}M\right)\frac{e\left(g,{\left(u^{\prime }\prod _{i=v}{v}_i\right)}^{rc}\right)}{e{\left(g_1,g_2\right)}^{c}e{(\left(u^{\prime }\prod _{i=v}{v}_i\right)}^{rc},g)},$$

(12)

$$=M$$

(13)

Next the generated key is optimized using hybrid fragment horde bland lobo optimization algorithm.

3.3. Hybrid Fragment Horde Bland lobo Optimization

3.3.1. Traditional PSO Algorithm

Three vectors are used in the computations of the PSO algorithm. They are x-vectors, p-vectors, and v-vectors, accordingly. The p-vector (pbest) designates the place where the particle has found the object, and the x-vector tracks the particle's current location inside the search zone. best response to yet. Particle velocity, which forecasts where each succeeding particle will move throughout the course of that iteration, is likewise included in the v-vector. Initial random displacement of the particles occurs in preset directions. Gradually altering the particle's orientation allowed it to start moving on its own in the direction of the prior best position. Then it searches the area for the optimal spots to perform some fitness-related tasks, using the formula $fit=S^m-S$. Here, the particle's position is specified as $\overrightarrow{M}\in s^m$, Although its speed is given as $\overrightarrow{w}$. These two variables are initially chosen at random, and they are subsequently updated repeatedly using the two formulas in Equation (14)

$$\overrightarrow{w}=\omega \overrightarrow{w}+c_1{r}_1\left(\overrightarrow{q}-\overrightarrow{M}\right)+c_2{r}_2\left(\overrightarrow{f}-\overrightarrow{M}\right)$$

(14)

The inertia weight, or $w$ in this instance, is a user-defined behavioural parameter that controls the degree of particle velocity recurrence. The particles implicitly interact with one another since the even before position of the particle (pbest position) isq, and the prior-best location of the particle inside the swarm (gbest position) is$\overrightarrow{f}$. The acceleration constants are $c_1,c_2$ and Utilizing the stochastic variables, this is graded. $r_1,r_2~U\left(\mathrm{0,1}\right)$. Equation illustrates that regardless of fitness gains, the particle is propelled to the next location in the search region by adding the velocity to its current position (15) as portrayed in Figure 6.

$$\overrightarrow{M}\leftarrow \overrightarrow{M}+\overrightarrow{w}.$$

(15)

3.3.2. Traditional GWO Algorithm

Hierarchical search agents exist in the GWO algorithm. Equations (16) and (17) use mathematics to illustrate how encircling occurs as grey wolves hunt their prey (17).

$$\overrightarrow{B}=\left|\overrightarrow{E}.{\overrightarrow{M}}_q\left(u\right)-\overrightarrow{M}\left(u\right)\right|$$

(16)

$$\overrightarrow{M}\left(u+1\right)={\overrightarrow{M}}_q\left(u\right)-\overrightarrow{H}.\overrightarrow{B}$$

(17)

Where $u$ the current iteration is handed to the term "coefficient vectors" is used to describe $\overrightarrow{H}$and $\overrightarrow{E}$. Grey wolves have a special ability to locate their prey and surround it. Using the heightened awareness of potential prey sites of alpha, beta, and delta wolves, these grey wolf hunting behaviours are statistically replicated. Regardless of whether the further solutions are necessary, the top three are taken into account. Below are the mathematical equations (18) through (24):

$${\overrightarrow{B}}_{\alpha }=\left|{\overrightarrow{E}}_1.{\overrightarrow{M}}_{\alpha }-\overrightarrow{M}\left(u\right)\right|$$

(18)

$${\overrightarrow{B}}_{\beta }=\left|{\overrightarrow{E}}_2.{\overrightarrow{M}}_{\beta }-\overrightarrow{M}\left(u\right)\right|$$

(19)

$${\overrightarrow{B}}_{\delta }=\left|{\overrightarrow{E}}_3.{\overrightarrow{M}}_{\delta }-\overrightarrow{M}\left(u\right)\right|$$

(20)

$${\overrightarrow{M}}_1={\overrightarrow{M}}_{\alpha }-{\overrightarrow{H}}_1.\left({\overrightarrow{B}}_{\alpha }\right)$$

(21)

$${\overrightarrow{M}}_2={\overrightarrow{M}}_{\beta }-{\overrightarrow{H}}_2.\left({\overrightarrow{B}}_{\beta }\right)$$

(22)

$${\overrightarrow{M}}_3={\overrightarrow{M}}_{\delta }-{\overrightarrow{H}}_3.\left({\overrightarrow{B}}_{\delta }\right)$$

(23)

$$\overrightarrow{M}\left(u+1\right)=\frac{{\overrightarrow{M}}_1+{\overrightarrow{M}}_2+{\overrightarrow{M}}_3}{3}$$

(24)

For despite their good performance, traditional algorithms can be improved upon to address their flaws and raise the bar.

3.3.2. Hybrid Fragment Horde Bland Lobo Optimization

Traditional algorithms can be improved upon despite having good performance to solve the shortcomings and raise the bar. There are some flaws in the conventional PSO algorithm, including inferior performance across a variety of fields. Along with these disadvantages, the GWO algorithm also has slower convergence, worse solution precision, and less effective local searching. Therefore, additional research is needed to enhance robustness and integration. These issues are dealt with in this work using a brand-new hybrid methodology. In the suggested Hybrid Fragment Horde Bland Lobo Optimization, the PSO method's criteria are blended with the GWO algorithm. Equations (15) and (16) illustrate the mathematical model of the prey enclosure in the proposed approach, whereas Equations (17) show the hunting strategy's mathematical model (18). The location has been updated, which is the main reformation in the recommended paradigm. Equation (19) illustrates the updating of the position in our Hybrid fragment horde bland lobo Optimization model, where $\overrightarrow{M}$ denotes the velocity for updating the location of PSO, as indicated in Equations (18) and (24).

$$M\left(u+1\right)=\frac{{\overrightarrow{M}}_1+{\overrightarrow{M}}_2+{\overrightarrow{M}}_3+\overrightarrow{M}}{4}$$

(25)

Again, in the classic PSO method, c1 and c2 are regarded as acceleration constants, however in the recommended Hybrid fragment horde bland lobo Optimization model $c_1$ and $c_2$ fluctuate in accordance with the values 0.1, 0.3, 0.5, 0.7, and 1. The optimal key is generated using the Hybrid fragment horde bland lobo Optimization. Algorithm 1 presents the ideal key selection based on Hybrid fragment horde bland lobo Optimization.

4. Results and Discussion

4.4. Performance Comparison

Based on clusters 2, 3, and 4, the Boost Graph convolutional network clustering method was evaluated in terms of time and memory for the SecPri-BGMPOP threshold values of 1, 2, and 4. The suggested system and the Boost Graph convolutional network clustering used in earlier work are then compared in terms of time and memory requirements. The Boost Graph convolutional network clustering recommended method produced improved outcomes, as illustrated in Figure 13.

When comparing the Boost Graph convolutional network clustering(BGCNC) -based SecPri-BGMPOP method to FCM clustering, it is discovered that Boost Graph convolutional network clustering performs better. Here, we've examined time and memory across various cluster sizes. When we examine the data, we discover that the proposed system uses less memory and requires less time than the current FCM-based technique (see Fig.10). As a result, our suggested Boost Graph convolutional network clustering approach performs better and produces better results.

Figure 14. Comparison of proposed Vs FCM Memory Measures for SecPri-BGMPOP threshold value “1, 2, 4”.

Figure 14. Comparison of proposed Vs FCM Memory Measures for SecPri-BGMPOP threshold value “1, 2, 4”.

Figure 15. Timing comparison graph for the proposed work in key generation, encryption phase and decryption phase.

Figure 15. Timing comparison graph for the proposed work in key generation, encryption phase and decryption phase.

The amount of time needed for computation in the key generation phase using the proposed MPBE, upgraded W09, Water 05, and MPBE curve. When compared to other methods, it can be seen that the Bibber approach requires less calculation time for the key creation, encryption, and decryption phases.

Table 4. Sanitization method and proposed SecPri-BGMPOP.

Table 4. Sanitization method and proposed SecPri-BGMPOP.

Performance metrics	Proposed versus previous approaches
	Sanitization method		SecPri-BGMPOP
Information loss	0.07%	0.024%
Throughput	3.5Mbps	7Mbps
	3.625Mbps	7.16Mbps
Encryption time	0.11s	0.0086s
Decryption time	0.054s	0.315s
Efficiency	46.87s	58.130s

Table 5. Performance enhanced Hybrid fragment horde bland lobo Optimization.

Table 5. Performance enhanced Hybrid fragment horde bland lobo Optimization.

HFHBLO	PSO	GWO	Attack
Better than	0.26%	0.23%	CCA
Superior to	0.40%	0.29%	CPA

The simulation thus shows that our suggested information security technique outperformed the currently used traditional algorithms based on particular assaults. As a result, it is evident from the simulation results that our sanitising strategy outperforms other standard algorithms currently in use. Protecting this type of data is essential since it is necessary to use sensitive diagnostic information about autism to determine if a person is autistic or not. This type of information is more relevant in the healthcare industry. This study's findings demonstrated that our suggested cleaning method protects these data against some threats more effectively than existing techniques. Nonetheless, it is indicated that, in terms of data security and privacy, the healthcare industry can make extensive use of our suggested strategy.

5. Conclusions

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