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ArticlesA Hybrid Solution for Secure Privacy-Preserving Cloud Storage & Information Retrieval
• Ankit Kumar1 , Turki Aljrees2 , Sun-Yuan Hsieh3 , Kamred Udham Singh4,5,* , Teekam Singh6 , Linesh Raja7 , Jitendra Kumar Samriya8 , and Rajesh Kumar Mundotiya9

Human-centric Computing and Information Sciences volume 13, Article number: 11 (2023)
https://doi.org/10.22967/HCIS.2023.13.011

Abstract

Cloud storage is an emerging archetype used by businesses for data storage. Clients require easy access to data in the cloud, which helps clients with limited computing power move their high-value, high-risk principle jobs to the cloud. The primary concern of this study is towards flawless verifying, checking data bundles, and determining modules to carry out the project. As discussed at several research platforms, each client is concerned about data storage and retrieval. Data security is critical in cloud computing, and thus monitoring has evolved into a vigilance that abstracts the monitoring process. Request made by clients for security planning is always computationally exclusive. As a result, each customer is concerned as to their verified condition. This paper will highlight a secure cloud communication method for client data to address the afore-mentioned issues. Synchronization creates secret hare keys, combined with a model-encrypted data packet sent to the cloud. These secure data are then sent over a public network, subject to monitoring to avert data leakage to an unauthorized party. They lack the cohesion to outsource data storage to the cloud deliberately. As an outcome of this model's distribution, the perception of authorized data owners would be changed regarding cloud storage up to 97.6%, addressing the trust issues between data owners and cloud ace affiliations.

Keywords

Cloud, Privacy, Storage, Information Retrieval, Security, Encryption

Introduction

Cloud storage is an emerging new typology of data storage applied by corporates to store their extensive data in the present scenario of cut-throat competition. Clients are eager to know and perform data submission and retrieval from the cloud. Customers with restricted computing resources can re-adjust their large estimate unparalleled principal employment to the cloud by focusing on distributed calculating [1]. Providing perfect verification, examining information bundles, and determining modules to carry out the project are the key concerns of this inquisition, and as such, it is being conducted. It can be gauged that each customer is concerned about storing and retrieving their data files. In the era of cloud computing, data security is increasingly critical. Because of this, monitoring has developed into vigilance, which isolates the complexity of the monitoring process from the observer. Security planning in response to client demands is always computationally too expensive because of many variables. Therefore, prospects are concerned about the verified condition while executing private requests. Accordingly, this article describes a safe technique of communicating client data via the cloud to address these concerns. Internetworking environment creates secret hare keys through the process of synchronization. This will be merged with a data packet transferred to the cloud through encryption performed by and mixed with the model. These secure data packets are subsequently transmitted through a public network, where monitoring is employed to prevent information leakage to an unauthorized individual or group of people. Information owners do not have the necessary cohesiveness [2] to outsource cloud storage on purpose. When the distribution of this model addresses the trust issues between data owners and cloud networks, data owners will change their attitude toward the collection of cloud stores, thereby overcoming the confidence difficulties between cloud owners [3]. In a cloud setting, the capacity to exchange data among several users is important for success. A private cloud is being proposed for usage in the cloud in order to protect patient information privacy. The encryption of data before it is saved on a cloud server helps to preserve the privacy of the health information that is kept on the cloud server [4]. By doing so, we may be able to restrict the user's ability to do keyword searches. Because it is difficult to search for plaintext in encrypted data [5]. A keyword search strategy is necessary once more in order to discover the encrypted source file, albeit this time the method is more complicated to implement. The most essential qualities of the system are its effective key management along with its safe storage and retrieval of sensitive information.

Data Privacy
Data owners are extremely cautious to keep their data anywhere other than inside their control constraints due to security weaknesses in looped figuring. Also, confirming data in remote regions has become a significant source of mental anguish for indispensable professionals. The investigation's primary focus is on data security in the cloud's organizational structure. For the most part, cloud customers are not aware of the security techniques acknowledged by cloud service providers since they are primarily concerned with disseminating information to a mass audience over the internet and supplier assurance [6]. It was believed that the request made by market sellers did not provide relevant and appropriate information, despite many efforts made by the government to assure the collection of information.

Attackers on cloud
The two prominent techniques of exploiting cloud archives exist in external assaults and attacks within the cloud's internal architecture (structure). In this case, the outside aggressors are software developers who ambush data coming from outside the jurisdiction of the cloud service providers. Officials from cloud computing organizations with genuine benefits over enlisting resources are considered inside aggressors. Data security continues to be questionable in the progression of conveyed processing with respect to the arrangement, construction, and association instruments [7] (Fig. 1).
Customers do not grasp where data is stored during registration appropriation because of dispersed figuring, which confuses them. The concept of distributed figuring confuses customers as to where their data is held upon registering. Customers must build security measures in the presence of CloudPro centers since the data confirmation segment has been altered to deter external attacks. To increase and supply data security in the future, masters and academia must build models for secret client data protection. This study analyzes and solves the exact issue potential customers encounter by providing data security models for cloud storage facility designs.

Fig. 1. Attackers on the cloud.

Research Goals and Objectives
The first goal is to develop a comprehensive information assurance system that will ease customer concerns while increasing the usage of cloud-based information storage and retrieval operations. Currently, information is considered a static piece that an insurance company cannot safeguard, so they must rely on each other. If public trust in cloud-based administration is not upheld, expanding the range of cloud-based services will be undermined [8]. This study is being done to overcome the issues mentioned above—goals of the study are to identify variables that affect information security, protection, and controllability in cloud systems. A second goal is to examine alternative solutions to issues of reliability. The information structure is novel and allows information to self-ensure and self-protect itself. This test will determine whether or not the proposed data flow system via the cloud meets the relevant information assurance criteria. The purpose of this project is to provide a fully secured and protected chain mechanism [9, 10] for data storage and retrieval over the cloud. This ensures that data is stored and retrieved safely from a cloud server.

Scope of the Paper
Conveyed figurative work warrants a significant amount of research. Everything from the establishment to the stage and programming was available on a pay-per-use basis. This notion is based on the fact that datasets are grouped in the cloud storage structure. Any security flaw in a customer's dataset stored in a cloud storage facility will undermine the client's faith in the system. Cloud master communities must verify that security breakdowns are prevented to earn their consumers' trust. Specifically, we'll be looking for information on dealing with cloud specialist centers that provide mysterious protection for their customers' data. There is the established assumption that cloud professional associations can never be trusted with unstable customer data and the cloud provider has ordered all of the customers' data. The proposed systems provide a key management scheme to improve efficient and privacy-preserving data storage and retrieval which are its most significant features. The key contribution of this paper is to encrypt data before it is stored on a cloud server and to provide a safe cloud storage platform for storing and retrieving data. The user may also utilize the system's keyword search capability to identify a suitable file for their purposes.

Related Work

The primary research query concerning cloud computing concerns how to ensure the order of a client's data on the cloud. Client data is stored in distributed storage providers, which the customer should validate. Distributed computing has proved to be a consistent success in information technology (IT) and will continue to dominate IT organizations [11] going forward. On the other hand, cloud computing is beset with incredible difficulties, and is shaping to become increasingly important [12] to secure the appropriate physical, canny, and employee security safeguards, particularly in the cloud data collection industry. In addition, in transmitting such huge volumes of data, it is possible that the arrangement of the data may not be completely dependable. This zone represents the investigation efforts relevant to maintaining the security of data in distributed storage. State-of-the-Art
This is a term referring to a cutting-edge model for rising preparation in which it is possible to use machines in far-reaching server ranches for passing on to organizations in a flexible manner [13]. As organizations have progressed to requiring large-scale shoddy figuring, cloud enrolling has emerged as a buzzword. There are several facts associated with circulated processing, including the possibility of guileful influence groups gaining access to information maintained through this evolution. Furthermore, cloud computing is a promising [14] technological development that standard professionals have recently become acquainted with. The use of vast volumes of data, electronic thinking, new information, and communication breakthroughs has prompted reasonable advancements and an increase in the size of the business workforce. As of now, cloud organizations are appointed with special system requirements for several forms of business connectivity. These requirements were met by several distributed registration configuration layers, such as establishment, stage, or programming as an organization, among others. As a result, the nature of information technology organizations has undergone dynamic changes, and associations have been arm-twisted to review their plans and consider implementing a suitable processing structure [15], which may contribute to attaining and improving business objectives.
Attribute-based encryption is a splendid preference for data affirmation in data re-appropriating structures, such as sent figures. The use of an encryption framework, on the other hand, may need some standard operations to be retrained over a mixed dataset, primarily in the field of data restoration. Attribute based data retrieval with proxy re-encryption (ABDR-PRE) is demonstrated in this study [16] to assure that both fine-grained get the chance to control and recover over the figure works while showing a property-based data recuperation using go-between re-encryption (ABDR). The proposed arrangement achieves fine-grained data delivery to the board by accepting the KP-ABE framework; a delegator can create the re-encryption key and look records for the figure works to be shared over the goal agent's attributes, as well as make the re-encryption key and look records for the figure works to be transmitted over the goal agent's attributes. The calculation of the Advanced Encryption Standard (AES) [17] is one of the world's most notable and widely utilized symmetric square figure estimate techniques. This estimator has a defined structure that allows it to encode and comprehend complex data, and is used in equipment and programming the world over to do this. In the case of AES computation, it is challenging for software programmers to obtain accurate data while encoding.
According to the current design, every bit of information and substance is being stored in the cloud owing to distributed capacity organizations. The vast volume of data collected from each customer may influence the delivered material. This strategy is commonly used to reduce the limited cost and resource needs of data benefits in the cloud by eradicating redundant information and storing only one copy of each piece of information. De-duplication [18] is most effective when many customers re-appropriate comparative data to the suitable stockpiling organizations, although it raises concerns about pursuit and security.
San Francisco-based Salesforce Inc. (https://www.salesforce.com) [19] is a distributed computing and social undertaking programming-as-a-service provider that specializes in customer relationship management (CRM). The Salesforce CRM item, which includes Sales Cloud, Service Cloud, Marketing Cloud, Force.com, Chatter, and Work.com, is its most prominent cloud stage and application. Salesforce is a cloud-based platform and application that allows businesses to manage customer relationships.
The sophisticated Salesforce platform's abilities continue to expand as more advanced technology develops. The ability to control sales connections progresses on pace due to implementing a cutting-edge CRM framework [20]. This research illustrates a range of recent technological developments that assist a multichannel approach to deal with the structure of successful customer relationships. Rather than maintaining a fleet of laptops and tablets, organizations opt to retire them to update their business groupings.
Cloud processing is described in this study, allowing clients to remotely store and retrieve their information based on interest management without the burden of local information hoarding and protection [21]. In any event, the guarantee of protecting the private information handled and created throughout the computation is increasingly becoming a legitimate security issue for everyone. In its most basic form, distributed computing enables clients with limited computational resources to redistribute their enormous calculation outstanding workloads to the cloud and financially benefit from the massive computational power, data transmission, stockpiling and even appropriate programming that can be reaped as compensation for each utilization method shown in Table 1.

Table 1. Comparative analysis of exiting work

 Study Service throughput Service availability Utilization/ scalability Application circumstances Limitations/gaps Dhaya et al. [17] - - Yes IaaS/PaaS, I/O-based operation performed Service authentication Dickinson et al. [18] Yes Yes Yes S3, Azure Required SSL certificate to validate Casola et al. [19] Yes Yes Yes AWS/ PaaS Limited platform Celiktas et al. [20] Yes Yes Yes GCB, run data sensitive app Complex architecture Tabassum et al. [21] - Yes Yes Service-oriented app Throughput is not reliable

This paper describes the monitoring measures so that the most excellent possible use of the could min algorithm may be made for the information. With the growth in internet traffic and demand for more resources, sketch-based solutions have been proven to reach greater levels of accuracy [22] for the same price as what conventional methods used to perform.
The proposed technique [23] aims to address a weakness in user clustering caused by a lack of comprehensive utilization of contextual information such as cloud service placement and an inefficient way for identifying the similarity of two vectors. The Scream dataset examination suggests a decrease in the cloud service recommendation process error rate.
Immutability, transparency, and a distributed structure are all advantages of blockchain technology, which serve to mitigate these disadvantages. An Internet-of-Things (IoT) solution based on blockchain technology is presented in this study to help identify intruders through virtual surveillance [24]. As a result of the blockchain-based tamper-proof data storage, this application has a significant advantage over other monitoring methods.
Deep learning has been extensively researched and deployed at the cloud and edge levels to enable accurate data analysis with minimal latency [25]. But studies have yet to address centralized administration, adversarial attacks, security, or privacy.

Research Methodology & Proposed Work

The main aim of this component is to introduce a few types of procedures utilized for accomplishing the destinations, and to answer the varying questions. The technique will introduce a secrecy security model to ensure client information in cloud frameworks [26]. We have included all of the investigations of ventures to fledgling in the research strategy, while issues are inquired about for achieving the privacy goals.

Methodology
Going for correct and gene unity of data is a prime need in a PC framework and correspondence systems. Single direction keygen cryptography is applied to encode a data packet into a fixed length and specify the marking. The counterfeit neural system permits consolidating, scattering, and compacting is the information succession of bits. Later, securing against man in the middle attacks and birthday assaults monitoring will be used. Previously, it was accounted for that generally utilized hash works as MD5 and SHA-1 [27] never gained security. So, we are leveraging neural system innovation to create hash codes to meet down-to-earth necessities. Sending and receiving data over this process is converted into a practical execution model developed by Salesforce.

Architecture
While developing this model, the system engineering team incorporates a theoretical model to describe the system's viewpoints, structure, and behavior. The model's design enables the project's execution, acquisition, maintenance, correction, and future development. The cloud archive framework's system architecture, depicted in Fig. 2, comprises numerous segments and their corresponding relationships.

Fig. 2. Proposed model architecture for data security.

Proposed Approach
The entire procedure is isolated into the following three different stages. One process is in the client intra-network, another one is on the cloud base module, and the third one is monitoring it. In this approach, the main idea is to keep the data in safe mode on the cloud to transfer a key-generated data packet to the cloud. Also, as seen by the different types of hackers on the network, a monitoring system is to be applied to check the sender's correct order at the receiver's end shown in Fig. 3.

Fig. 3. Proposed workflow chart to preserve data privacy over the cloud.

Key generation
During the first stages of the synchronization process, the tree parity machines of A and B begin with weight vectors w_iA/B that have been picked at random and are not correlated. A random K public input vectors x_i is created in each time step, and the matching output bits A/B are computed for each of the K time steps. Following that, A and B send their output bits across the network. If they differ, τ_A≠τ_B, the weights are not modified [30]; otherwise, they are. A neural system-based learning rule suited for synchronization of mystery key age system must be implemented if none of the preceding learning rules is applicable. The weights of the machines are equal once they have been synchronized. They can be used in the construction of a shared key pair. In any case, only weights are affected by these learning rules, which are stored in hidden units with the σ$_i$=$τ_i$ formula. The Hebbian learning rule concept is applied in this study as follows:

(1)

Module for sending and retrieving data
A cloud computing and social undertaking programming organization regulates contact information and directions online for persistent customers. A module has been made to send the key delivered group and recoup it affirmed. This module has been developed by Salesforce. The main focus of this module is to fetch the value of the mystery key stored in a file and associated with each record that moves to the cloud. Later, when the data records were accessed, the key was compared with the saved vital file, and it did get the record of being open; otherwise, it indicated an error.

3.3.3 Secure count-min sketch
The security issue in network a refreshed sketch-put together calculation based concerning count-min empowers to secure traffic checking over the cloud. The count-min sketch query is a query operation that seeks the maximum or minimum data among the data items gathered in the specified epochs and region [31]. As an outcome, the following MAX/MIN inquiry, represented by a triple tuple, will be considered:

(2)

where MAX, MIN represents the query type, T the set of requested epoch numbers, and denotes the queried sensor node IDs indicating a query region. For example, Q=(MAX,t,{$s_1$,$s_4$,$s_6$,$s_{11}$ }) where query Θ∈{ MAX, MIN} seeks the highest volume of data gathered by sensor nodes Γ⊆{$s_1$,$s_2$,…,$s_n$}. In epoch t, we will concentrate on the basic MAX query targeted at one cell (M,{$s_1$,$s_2$,…,$s_n$}) and one epoch t; that is, Q=(MAX,t,Γ), where, Γ⊆{$s_1$,$s_2$,…,$s_n$}. Other complex searches spanning many epochs and cells can be readily decomposed into multiple simple ones. As seen on system traffic, the observing assignment is to follow the recurrence of the data packet by applying the refreshed count-min the sketch calculation's actualized hash capacity that will slam into an IP address of related utilized. After the related emit key, the bundle is anticipated by the aggressor because it is not ready to fit for foreseeing the idea of parcel information [32], just by watching the parcel aggressor not prepared to perceive the structure while knowing about observing calculation. Due to the key being obscure to the aggressor, it turns inexhaustibly unbending to the aggressor to crash. By looking for the base value, the proposed analysis determines the worth followed on a single information structure. The sum of all estimations is the value returned by the system, which figures the target counter for each section, to be examined of the data structure [33], and later re-establishes the base worth found for determining a real or fake IP.

Base Algorithm
Two base algorithms are to be taken to implement the concept, one from key generation and another from monitoring.

Neural key exchange algorithm
The collaborative learning synchronization of two neural networks may be utilized to create the neural key exchange protocol. The main difficulty is determining how to evaluate synchronization without a weight vector. All previous approaches delay evaluating synchronization, which impacts the security of the neural key exchange. An enhanced approach for measuring the synchronization of neural networks is provided to analyze the synchronization more rapidly and precisely. Firstly, the frequency with which the two networks have the same output in previous phases is utilized to measure their degree overall. Secondly, when the degree surpasses a certain threshold, the hash function determines whether the two networks have achieved full synchronization. The enhanced approach can find full synchronization between the two networks using only the hash value of the weight vector. There are K perceptrons with separate receptive fields in each of the hidden units. Each neuron has N inputs and 1 output for N neurons (Algorithm 1). The input is entirely binary as denoted below:

(3)

Additionally, discrete values between -L and +L establish the input and output mapping.

(4)

When the indexes j=1, N are used to denote the vector elements. The indexes i=1..., K denotes the ith remote unit of the tree parity machine, and the indexes j=1..., N denote the vector elements, respectively.

(5)

The output $σ_i$ of the i-the hidden unit is then defined as the sign of $h_i$ as follows:

(6)

As a result, if the number of inactive hidden units with _i=-1 is even (=+1) or odd (=-1), the value merely shows that fact. As a result, there are 2 (K–1) distinct internal representations (1, _2, … K), all of which lead to the same output value. For a situation where there is just one remote unit, the symbol is equivalent to 1. As a result, the tree parity machine with K=1 exhibits the same behavior as a perceptron, which may be seen as a specific instance of the more sophisticated neural network described above.

Algorithm 1. Neural key exchange algorithm
Input: Parameters: Input layer, hidden layer
Output: Secret key for critical exchange to share the data
Procedure: Start
Step 1: Initialized the input parameters of the neural network;
Step 2: Initialized randomly to all network weights of hidden layer;
Step 3: Calculate the input and feed and compute their weight;
Step 4: Repeat Steps 4 to 7 until synchronization occurs in the network;
Step 5: Compute the input of hidden layer;
Step 6: Exchange of output bit between two machine A and machine B;
Step 7: Comparers the output vectors of both the machines are identical, i.e., τA = τB.
Step 8: The Hebbian learning rule, the anti-Hebbian learning rule, and the random-walk learning rule are all used to modify the weights of the corresponding variables.
Step 9: After perfect sync, the synaptic weights in both networks are the same as one another.
Step 10: Computed weights are used as a secret key.

Count-min sketch algorithm
When presented with a data stream featuring the count–min sketch functions as a probabilistic data structure that serves as a frequency table of events, the mapping of events to frequencies is accomplished by using hash functions (Algorithm 2).

Proposed Algorithm
Execution of complete circle of data over the cloud using both neural key exchange and count-min sketch algorithms is to be modified and attached with the Salesforce application. Customers often access public, commercial cloud services through the internet, whereas private/ hybrid cloud services must be accessed over the company's internal network. A service's total performance is reflected in its cloud infrastructure and the network that connects it to end users. A user's perspective on cloud service performance should therefore consider both service and networking.

KeyGen algorithm
A persuasive method to accomplish the objectives is to substitute the first hash work by putting an arbitrary key worth using info. as a solid key. It becomes difficult for beast power to make hash impacts with the goal. Because system observing is frequently required to be dynamic, it must be changed at each association at runtime. If it is necessary to re-establish the key as precision profits by keeping this value low, the sum of lines of the sketch could be a decent measurement to choose. Counters on their way to reaching their maximum value should also cause a significant shift. As a result, it is impossible to imagine exchanging the key and using the same information structure because two identical objects would be assigned to different locations. Consider a switching period to be the time between two events in which a switch of some kind presses a comparable key. After each checking period, an essential recharge should be performed, while the information structure should be copied to an alternate memory before the key is discarded (Algorithm 3).

Algorithm 3. KeyGen algorithm
Input: Number of Input Layer, hidden layer
Output: Generated secret KeyGen
Step 1: Fixed the value of k as 8, hidden layer auto increment in n and set input layer units in Step l;
Step 2: The network weights to be initialized randomly;
Step 3: Repeat Steps 4 to 7 until status becomes zero;
Step 4: The inputs of the hidden units are calculated.
Step 5: The output bit is generated on 32 bits indicating the length of the key.
Step 6: A time slap is calculated on each slot on a generated key.
Step 7: Weights are modified using the Hebbian learning rule till symbolization does not occur.
Step 8: After complete synchronization, the synaptic weights are stored in a secret key file.

Updated monitoring algorithm
In this regard, it should be noted that estimations that rely on more than one information structure will not have the same exactness guarantees as to the first count-min calculation, with most of the error of the count-min calculation increasing in proportion to the number of information structures on which the estimation is predicated. However, because item values are often obtained on an as-needed basis for a limited time period, the executive only has to preserve the put-away information structures still necessary for the estimations on hand. The more established ones can be removed, ensuring that the accuracy is only slightly altered in a limited way overall. In contrast to a hash table, this approach uses sub-linear space to count the number of times a frequency occurs. There are “w” columns and “d” rows in a matrix, which is made up of that. The parameters establish the trade-off between precision and the limits of space and time on the system. Each row has a hash function that is connected to it. When an element is received, a hash for each row is discovered. The index of the matching row in the table is increased by one. The worth returned by the approach is the sum of all of the assessments made by the participants. According to the procedure, the objective counter is calculated to be examined once for each line of the information structure. The base worth is estimated once those gains are obtained (Algorithm 4).

Implementation and Results

This section summarizes the results obtained from implementing the experimental output. The algorithms proposed to execute the keygen and results obtained through MATLAB, with the method for locating interruptions provided. Finally, the test is displayed, and discussions about the execution of the discovery strategy are presented [29, 34]. Also attached is a screenshot of the developed module from Salesforce, indicating our real-life execution task. Based on their research methodologies, the currently available approaches to evaluating cloud service performance are divided into the two categories of measurement-based approaches and analytical modelling-based approaches [35]. The latter includes queueing theory-based, network calculus-based, and other stochastic models such as SRN-based methods.

Table 2. KeyGen of input units

 Parameter Value Input unit = 1 t 0.5676 No. of hidden units 8 Length of key 20 No. of iterations 786 Generated key @@@@@@@@@@@@@@@@@@@@ Input unit = 2 t 0.3913 No. of hidden units 4 Length of key 20 No. of iterations 464 Generated key $) %! #&%# %#'%#&%#%$( Input unit = 3 t 0.8682 No. of hidden units 4 Length of key 20 No. of iterations 787 Generated key %! $)#$)"  $'%)"!!&!& Input unit = 4 t 0.7271 No. of hidden units 4 Length of key 20 No. of iterations 534 Generated key &&-) #)/0)!'- (&*%'("$ Input unit = 5 t 1.208 No. of hidden units 4 Length of key 20 No. of iterations 749 Generated key # (, /- (#%%!"*) "%&&+ Input unit = 6 t 4.1877 No. of hidden units 4 Length of key 20 No. of iterations 2,239 Generated key &'-) %$(,$&- (&)) (() Input unit = 7 t 3.4139 No. of hidden units 4 Length of key 20 No. of iterations 1,604 Generated key /-1.-311+--,'*+. ()) Input unit = 8 t 11.5521 No. of hidden units 4 Length of key 20 No. of iterations 4,850 Generated key /, -*'-14-+*012/0, -'+

Monitoring Algorithm Output in Different Phases
In addition, the uncertainty related to the estimations produced by a secured sketch, as indicated in Tables 3–6, respectively were analyzed in MATLAB. In this example, five IP packets are evaluated as a sample of traffic gathered on the network's access point to the internet, as depicted in Figs. 5 and 6.

Fig. 4. KeyGen time versus input unit.

 (Value of X) (Value of Y) 15 6 9 16 4 12 17 4 13 18 10 8 19 3 16
Fake IP add: 582 515 418 0 0 0 0 0 0 0. Total number of errors is 3.

Table 4. Sketch values
 831 931 531 631 731 131 331 431 782 982 682 382 882 482 282 182 333 433 633 833 733 233 933 133 784 284 184 384 684 884 484 584 615 415 715 215 915 815 115 315 286 486 386 986 886 686 586 786 697 297 797 497 997 597 397 197 218 318 618 518 818 718 118 918

Table 5. Random generated IP
 234 982 234 758 297 365 0 0 0 0 436 983 983 154 0 0 0 0 0 0 492 734 492 492 0 0 0 0 0 0 818 818 243 818 818 454 116 243 116 454 582 515 418 0 0 0 0 0 0 0

Table 6. Counter values
 6 4 1 3 3 2 2 1 3 3 3 2 1 3 5 1 2 3 2 3 3 1 1 1 7 5 2 3 3 2 4 1 5 1 2 1 3 2 3 1 5 2 1 3 2 2 2 1 3 8 1 1 1 1 1 4 6 4 3 3 4 2 2 1

The value of k is 3 8 3 8 1 8 9 1. The randomized packets executed in the cloud pathway, through which the network switch could process them all, are shown in Fig. 6. Each packet is monitored and the sketch for the item is queried. This process provided an estimated frequency and the true frequency specifying the error. Analysis of experimental data and comparisons with the existing system [23] resulted in showing that it improves the storage efficiency by 3.6%.

Fig. 6. Resultant of valid IP.

Conclusion and Future Scope

As seen in the real world, most businesses and customers individually lack the infrastructure needed to keep their data safe on the internet. With cloud storage prices getting prohibitive, it is becoming increasingly attractive to use cloud storage for various purposes cost-effectively. The proposed framework provides more robust security because it is not helpless against any known leap. We could hear about a beneficial system built in such a manner that it accomplishes input/ yield protection, clever flexibility, and productivity in the cloud, among others. While data is stored in a cloud repository, it is kept encrypted. For a user to access the cloud and download encrypted data, they must first obtain an access key. With the correct access key, the user will access the cloud repository and download the encrypted data to a local computer installed at one’s location. To decode this information, the user will also need a secret key. As such, it is advised that users encrypt data at their end before uploading it to cloud storage servers to keep costs down and confidential material safe from prying eyes. This paradigm enables the user to keep track of secret keys they have created. As for the duty for encryption, now the onus is on the user for crucial management and critical storage. This model works on specific attack models and manages the cryptographic key, especially its distribution, to mitigate the attack. Thus, it is suggested to analyze the behavior of more cloud models and their loopholes related to data privacy.

Author’s Contributions

Conceptualization, AK, KUS. Investigation and methodology, TA, LR. Supervision, SYH. Writing of the original draft, AK, KUS. Writing of the review and editing, TA, KUS, JKS, RKM, TS, SYH. Formal analysis, TS, LR.

Funding

None.

Competing Interests

The authors declare that they have no competing interests.

Author Biography

Name: Ankit Kumar
Affiliation: Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, India
Biography: Ankit Kumar is an assistant professor in the Department of Computer Science at the SKIT in Jaipur, India. He holds a master's degree in technology from the Indian Institute of Technology Allahabad and is now pursuing a doctorate at the Birla Institute of Technology. He has published several articles in national and international journals. Information security, wireless sensor networks, cloud computing image processing, neural networks, and networks are the areas where his work has been highlighted

Name: Chetan Swarup
Affiliation: Department of Basic Science, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh-Male Campus, Riyadh,Saudi Arabia
Biography: Chetan Swarup has been working as senior assistant professor in the Department of Basic Science at Saudi Electronic University, Riyadh (KSA) since 2015. He received his Ph. D. in Operations Research from CCS University, Meerut (India) in 2009. His research interests are in the field of Optimization Techniques, Differential Equation, Integral Equation etc. He has published a number of research publications in peer-reviewed journals.

Name: Sun-Yuan Hsieh
Affiliation: Department of Computer Science and Information Engineering, Institute of Medical Information, Institute of Manufacturing Information and Systems, Center for Innovative FinTech Business Models, and International Center for the Scientific Development of Shrimp Aquaculture, National Cheng Kung University, No. 1, University Road, Tainan, Taiwan
Biography: Sun-Yuan Hsieh received the PhD degree in computer science from National Taiwan University, Taipei, Taiwan, in June 1998. He then served the compulsory two-year military service. From August 2000 to January 2002, he was an assistant professor at the Department of Computer Science and Information Engineering, National Chi Nan University. In February 2002, he joined the Department of Computer Science and Information Engineering, National Cheng Kung University, and now he is a chair professor. He is Fellow of the British Computer Society (BCS) and Fellow of Institution of Engineering and Technology (IET). Dr. Hsieh is also an experienced editor with editorial services to several journals.

Name: Kamred Udham Singh
Affiliation: Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
Biography: Kamred Udham Singh received a Ph.D. from Banaras Hindu University, India in 2019. From 2015 to 2016, he was a junior research fellow, and from 2017 to 2019, he was a senior research fellow with UGC (University Grant Commission), India. In 2019, he became an assistant professor at the School of Computing, Graphic Era Hill University, India. He is currently a post-doctoral fellow at the CSIE, NCKU, Taiwan. His research interests include image security and authentication, deep learning, medical image watermarking, and steganography

Name: Teekam Singh
Affiliation: Department of Mathematics, Graphic Era Hill University, Dehradun, India
Biography: Teekam Singh is an Assistant Professor at Department of Mathematics, Graphic Era Hill University, Dehradun, India. He has obtained PhD from Indian Institute of Technology Roorkee (IITR), India, and Master of Technology from Jawaharlal Nehru University (JNU), New Delhi, India. He has four years of teaching experience in the field of Mathematics, Applied Mathematics and Theoretical Computer Science. His research area includes scientific journal Medical Image Processing, Machine Learning, Computer Simulation and Mathematical Biology.

Name: Linesh Raja
Affiliation: Department of Computer Application, Manipal University Jaipur, Jaipur, India
Biography: Linesh Raja is currently working as Assistant Professor at Manipal University Jaipur, Rajasthan, India. He earned a PhD in computer science in the year 2015. Before that, he has completed his Master and bachelor’s degrees from Birla Institute of Technology, India. Dr. Linesh has published several research papers in the field of wireless communication, mobile network security in various reputed national and international journals. He is recently appointed as managing editor of the Taru Journal of Sustainable Technologies and Communication. He has edited the Handbook of Research on Smart Farming Technologies for Sustainable Development, IGI Global.

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Ankit Kumar1 , Turki Aljrees2 , Sun-Yuan Hsieh3 , Kamred Udham Singh4,5,* , Teekam Singh6 , Linesh Raja7 , Jitendra Kumar Samriya8 , and Rajesh Kumar Mundotiya9, A Hybrid Solution for Secure Privacy-Preserving Cloud Storage & Information Retrieval, Article number: 13:11 (2023) Cite this article 1 Accesses