Blockchain based mobile communication data privacy protection algorithm for cellular networks

Abstract

The rapid development of mobile communication technology not only brings great convenience to users, but also brings the risk of user data privacy leakage. Due to the broadcasting nature of mobile communication transmission, open wireless interfaces have become a security vulnerability for mobile devices such as mobile phones, which can be easily eavesdropped. This article studied a data privacy protection algorithm suitable for mobile communication in cellular networks based on the anonymous mechanism of blockchain. This article first analyzes the overall framework and anonymity technology of blockchain data from the perspective of privacy and queryability. Then, based on blockchain technology, consistency mechanisms, privacy control, and access rights management are designed. Finally, mobile communication data privacy protection algorithms are designed and implemented through data encryption and verification. The latency of transactions under different task volumes and throughput at different nodes were analyzed to verify the reliability of several anonymous mechanisms in cellular network mobile communication data privacy protection in blockchain. Based on the experimental results, it was concluded that the reliability range of CryptoNote and the zero coin and zero currency protocol mechanism in the specified nodes was between 92 and 99%. This article utilized blockchain technology to distribute mobile communication data in nodes and achieve decentralized data transmission, thus protecting the privacy of wireless network communication data. By analyzing the reliability of anonymous mechanisms based on blockchain in mobile communication nodes, it was concluded that this method had certain research value.

Keywords

Cellular networks mobile communications data privacy blockchain technology privacy protection byzantine algorithm

1. Introduction

With the continuous development of mobile communication technology, people’s dependence on mobile phones is also increasing. However, with the development of mobile communication technology, the leakage of a large amount of information poses a serious threat to users’ personal privacy. In mobile communication, traditional privacy protection methods are difficult to meet the requirements of practical applications, and there is an urgent need for more secure and efficient privacy protection methods. Blockchain is an information processing technology with distributed and decentralized characteristics, which can to some extent solve the problem of data privacy protection [1].

At present, cellular network mobile communication has brought great convenience to people’s lives, but it also brings issues of data privacy protection. Peng Li proposed a data privacy protection algorithm with redundancy mechanism based on slicing technology to address packet loss issues, which ensured privacy through privacy homomorphism mechanism and carried hidden data to ensure redundancy [2]. Bin Gu believed that most existing joint learning algorithms for data were limited to synchronous computing. When there are generally unbalanced computing and communication resources among all parties in the Federated learning system, it is crucial to develop asynchronous training algorithms for data while maintaining data privacy in order to improve efficiency. At present, there is a lack of theoretical analysis to quantify the degree of data privacy protection, and an optimal average consistency algorithm is proposed for the damage of data privacy [3]. Jianping He believed that the average consistency of privacy protection aimed to ensure the privacy of the initial state and the asymptotic consistency of the accurate average of the initial values, which was achieved by adding variance attenuation and zero sum random noise during the consistency process. Through theoretical analysis and extensive simulations, this method enables efficient and accurate attack detection while protecting sensitive information in each domain [4]. Liehuang Zhu proposed a cross domain attack detection method for software definition, which was based on a joint protection method of interference password and data password, and also improved the KNN (K-Nearest Neighbor) to achieve better efficiency. Thus minimizing the information obtained by the attacker from the collected user historical location information [5]. Li Jie designed a location privacy protection algorithm based on a probability inference model. First, he used the Hidden Markov model to model the user’s motion state and location release, and calculated a suppressed release probability vector for the user’s motion location. He then implemented suppressed publishing for some users’ positions based on the probabilities contained in this probability vector [6]. Wang Hong applied an algorithm based on density clustering to differential privacy protection, solved the sensitivity problem of data parameter input, and proposed an improved algorithm for differential privacy protection [7]. The algorithms used in the above studies have shown good performance, but some of the research content has issues such as poor adaptability.

Blockchain technology, in the form of the fourth technological revolution in human society, has opened a new chapter in redefining the way human social values are transmitted. At the same time, more and more research is applying blockchain technology to cellular network mobile communication data privacy protection. Zice Sun proposed a double disturbance local differential privacy algorithm to interfere with workers’ location information. All sensor data is uploaded to the blockchain through edge nodes, processed by the edge cloud and fed back to the requester [8]. Bin Luo believed that distributed k-anonymity, as one of the most popular privacy protection methods, fails to take into account the credibility of participants, leading to malicious tracking of vehicles, leakage of sensitive information, and even threats to personal property security. To address this issue, he proposed a blockchain based trust location privacy protection scheme in VANET (Vehicular ad hoc network) [9]. Cheng Xu proposed a blockchain based privacy protection scheme for sensitive data of connected vehicles to address the potential threat to user privacy caused by relatively low security of connected vehicles. Safety analysis indicates that specific data ensures the confidentiality of critical data from each data provider and protects the parameters of the model used for data analysis [10]. Bao Le Nguyen used blockchain technology in his research to create a secure and reliable data exchange platform across multiple data providers, where Internet of Things data was encrypted and recorded in distributed ledgers. It shows that the application of protocol in blockchain has low computational cost and good information concealment [11]. Liu Feng constructed the framework of the protocol and conducted formal argumentation and calculus, demonstrating that the protocol can be combined with blockchain networks to effectively fuse and sign various privacy information in an anonymous state. At the same time, he also analyzed the properties and security of the protocol [12]. The above research indicates that blockchain technology has certain effects in protecting data privacy in mobile networks, but currently relevant research is still researchable. Blockchain technology has high security and confidentiality, but current mobile communication data privacy protection algorithms still lack reliability in data decentralized transmission.

With the development of mobile communication technology, cellular mobile communication tools are comprehensively affecting people’s work and life, and there are many security and confidentiality risks in their use. At present, a large number of security risks have emerged in real life. After analyzing and classifying mobile communication networks, they may face dozens of different types of security threats, among which the most common are eavesdropping, impersonating communication participants, impersonating users, and stealing mobile phones. Figure 1 above shows the formal system model structure of this paper.

Figure 1.

System model.

This article studied the privacy protection algorithm for mobile communication data in cellular networks based on blockchain. A data security issue based on PBFT (Practical Byzantine Fault Tolerance) accessibility was studied through the privacy and queryability of blockchain data [13]. In Fig. 1, this article categorizes cellular network mobile communication into two categories: big data and data privacy. The data protection decision objectives are divided into mathematical models, reliability rates, and graphical models. Based on the statistical model, the design of data privacy protection algorithms and their implementation and performance evaluation are studied. The blockchain consensus layer mechanism was rated, and the encryption, decryption, and digital signature authentication processes were analyzed in the design and implementation of mobile communication data privacy protection algorithms. The experimental part verified the reliability of several anonymity mechanisms in cellular network mobile communication data privacy protection by analyzing the transaction latency and throughput at different nodes under different task volumes. From the research results, it is recommended that CryptoNote be used as a research method for privacy protection of mobile communication data in cellular networks.

2. Architecture design for data privacy protection in network mobile communication

2.1 Overall architecture of blockchain

This article aims to study a data security issue based on PBFT accessibility, starting from the privacy and queryability of blockchain data. In terms of subjective will and resistance to attacks, Byzantine distributed security is the strongest among all consistent algorithms [14]. Table 1 provides a rating of the blockchain consensus layer mechanism to provide a research basis for privacy protection in the following text.

Table 1
Score of blockchain consensus layer mechanism

		PBFT	POW (Proof of work)	POS (Proof of stake)	VRF (Verifiable random function)
Node licensing		$\surd$	$\times$	$\times$	$\surd$
Delay		9	4	8	8
Throughput		8	3	6	6
Energy consumption		8	2	7	5
Extensibility		3	8	5	2
Overall evaluation	Total score	28	17	26	21
	Average score	7	4.25	6.5	5.25

Table 1 evaluates the Byzantine fault-tolerant mechanism, Proof of work mechanism, proof of stake and VRF in the consensus layer mechanism. It can be seen that Byzantine fault tolerance and VRF require node license, while Proof of work mechanism and proof of stake do not require node license. In terms of delay, throughput, energy consumption and extensibility, the total score of Byzantium fault tolerance is 28 and the average score is 7; the total score of Proof of work mechanism is 17 and the average score is 4.25; the total score of POS is 26 and the average score is 6.5; and the total score of VRF is 21 and the average score is 5.25. Therefore, Byzantine fault-tolerance has the highest rating among the four consensus mechanisms mentioned above, which also provides evidence for the adoption of this method in the previous text.

At present, there are often problems with non functioning nodes, error messages, and malicious nodes in blockchain networks [15]. The key to consistency mechanism is to achieve consistency between normal nodes. Generally, nodes with incorrect information are considered Byzantine nodes, while ordinary nodes are non Byzantine nodes.

The assumptions widely applied to the Byzantine system include:

(1)

The actions of Byzantine nodes may be arbitrary and may collude with each other;

(2)

The error between nodes is independent;

(3)

Due to asynchronous communication between nodes, information in the network may become chaotic and lost. However, most protocols assume that information can be transmitted to the target within a certain amount of time;

(4)

The information transmitted between servers can be detected by third parties, but the content of the information cannot be tampered with or forged, and the integrity of the information cannot be verified.

Based on the assumptions of the Byzantine system mentioned above, this article further explores that the original Byzantine fault-tolerant system does not play a significant role in practical applications, and the computational complexity continues to increase over time. The adoption of the Byzantine fault-tolerant system reduces the computational complexity of the Byzantine protocol from an exponential level to a polynomial level. The biggest feature of the Byzantine style practical fault-tolerant algorithm is retrievable encryption, and its core idea is to ensure the security of data in the blockchain with minimal computational cost through the retrieval of encrypted ciphertext for key information [16].

On this basis, the infrastructure of blockchain technology itself has been deeply studied to achieve effective protection of blockchain user privacy. Figure 2 shows the basic architecture model of blockchain.

From Fig. 2, it can be seen that the blockchain architecture is divided into six basic layers: data layer, network layer, consensus layer, incentive layer, contract layer, and application layer. Each layer has independent and collaborative functions to achieve the normal operation of various applications based on blockchain technology [17]. The function of the lowest level data layer is to store and extract data. When each transaction is completed, all data related to that transaction is packaged into a data layer and transmitted as a separate data block to the specific database. In general, in a data block must include time data, hash value and transaction data. The consensus layer enables dispersed multiple nodes to achieve the goal of ensuring data correctness; The role of the incentive layer is to provide rewards for each node to actively participate in the security verification of blockchain; The contract layer includes various script codes, algorithm mechanisms, smart contracts, and so on; The application layer provides a detailed explanation of the application of blockchain in different fields.

2.2 Blockchain anonymous technology

At present, the reason for using blockchain is mostly due to its anonymous nature, which can provide users with distributed node and address transactions. However, public accounting also poses certain risks for users to rely on record analysis for association relationships [18]. Table 2 shows the performance comparison of blockchain anonymity technology.

Table 2
Performance comparison of blockchain anonymous technology

	Type	Means of defense
Pseudonym	A	Blocking communication
Mixed currency	A	Confusion handling
CryptoNote	B	Ring signature
Zero coin and zero currency agreement	C	Confusion handling

Figure 2.

Basic architecture model of blockchain.

Table 2 analyzes several blockchain anonymity technologies mentioned below, and this article briefly discusses their performance here. Pseudonym uses other symbols to replace the user’s account in transactions, thereby severing the direct connection between the user’s true identity and transaction activities. During the transaction process, the blockchain system does not use its account to directly store and manage user transactions. Due to the fact that each transaction has multiple input and output locations, each transaction can have multiple addresses, resulting in a new address for each transaction to achieve anonymity. Mixed currency is the process of combining different people’s information in a specific way. Due to each user having their own unique trading behavior, a large amount of data can be used for analysis to reduce the degree of irrelevance in transactions. This method involves placing multiple traders together, making their relationships blurry.

At present, there are two main types of mixed currency systems. One is based on a distributed node mixed currency mechanism, and the other is based on a mixed center mixed currency system [19]. Distributed mixed currency refers to the construction of mixed transactions between trading users through messages, without the involvement of trusted third parties. To distinguish it from ordinary nodes, the dash network also has a main node for handling anonymous transaction requests. When anonymous transactions are initiated, all transactions are divided into several smaller transactions, which are then mixed with other transactions and recorded through blockchain. By dividing a transaction into multiple nodes for fusion, it can prevent tracking of this node to improve anonymity. Hybrid currency is an effective method in improving blockchain anonymity. The mutual distrust among virtual currency users in a distributed virtual currency system leads to information leakage between virtual currency nodes, and the security of nodes themselves in the virtual currency system becomes a bottleneck that restricts the anonymity of the virtual currency system [20]. Through the analysis of metadata and patterns for mixed coins, the analytical link network diagram can be obtained to destroy the non-relevance of transactions.

In CryptoNote, the sender of a transaction is divided into a user group, where a ring signature is executed on the transaction. The verifier can only confirm that the sender belongs to the user group, but cannot determine the sender’s specific identity, thus enabling the anonymity of the sender. Each transfer uses a key to calculate a temporary confidential address. As this method requires the use of the recipient’s private key, no third party engages in illegal theft. At the same time, it also associates the key generated by the sender with the recipient’s implied address, thus achieving the purpose of anonymity for the recipient.

For message I, the signer ID (Identity document) is randomly selected as $u_{i}$ , and the following formulas are calculated:

$\displaystyle U_{i}=u_{i}p$ (1) $\displaystyle X_{v}=x_{v}Q_{v}-\sum\limits_{i=1}^{n}{(U_{i}+h_{i}Q_{i})}$ (2) $\displaystyle h_{i}=H_{2}(I||L||U_{i})$ (3) $\displaystyle V=(u_{v}+h_{v})\sum\limits_{j=1}^{k}{D_{vj}}$ (4)

Ring signature $\sigma$ is output, and the signer calculates $T$ and knowledge signature $R$ , saving ( $T, R$ ) as backup evidence [21].

The signature verification algorithm is as follows [22, 23].

The signature ID can be used (T, R) to prove that signature $\sigma$ is signed by oneself, and the verifier calculates:

$\displaystyle h_{v}=H_{2}(I||L||U_{v})$ (5) $\displaystyle Y=\sum\limits_{j=1}^{k}{Y_{j}}$ (6)

Then, the validity of the following two formulas is verified [24]:

$\displaystyle e(Y,T)=e(P,V)$ (7) $\displaystyle e(P,T-h_{u}H_{1}(\textit{ID})=e(R,H_{1}(\textit{ID}))$ (8)

If all are valid, it is confirmed that the true signatory is ID.

At the same time, concealing transaction amount information enhances the confusion of ring signatures. In CryptoNote, ring signatures are used to achieve the anonymity of the sender, and random keys are used to achieve the anonymity of the receiver, greatly improving the irrelevance of transactions and enhancing the anonymity of the system.

In the zero coin and zero currency protocols, this article suggests adding a process for minting currency on the existing Bitcoin network. Currency can be destroyed in the original process and replaced with a new currency, making it difficult for attackers to analyze the transaction. However, its essence still confuses users’ Bitcoin and it can only achieve the anonymity of the sender, while the recipient and transaction amount are still public. By encrypting the addresses of the sender and receiver, and encrypting the balance in the wallet, anonymous communication is possible. Since transaction messages do not contain information such as sender, receiver, and transaction amount, they have a high degree of anonymity. The problem is that there is a large amount of extra time and computational overhead, and there are still deficiencies in the operation efficiency of the system. In the actual use process, only less than 20% of the transactions use anonymous transactions, and the scalability of the anonymous system is poor.

2.3 Design of blockchain consistency mechanism

2.3.1 Data consistency assurance

The data storage of blockchain has both physical dispersion and logical unity. This mechanism achieves global consistency of data and data consistency between nodes. In blockchain storage systems, the authenticity and correctness of accounting information largely depend on the credibility of accounting information. Compared with the Centralisation storage system, the blockchain system is open and transparent, decentralized, traceable and tamper proof.

A block header can encapsulate information such as the hash value, timestamp, random number, and Merkel root of the previous block. The block mainly records transactions in detail, which includes information such as the input and output of the transaction, as well as the transaction value. The block is divided into a series of chain structures. The above features make the data in the blockchain unforgeable and tamper resistant, while also ensuring the consistency of the data in the blockchain. The comprehensive sharing of accounting information has been achieved throughout the entire blockchain system. This method utilizes chain structure and encryption technology to achieve links between blocks to ensure data security and consistency. In the blockchain mechanism, each participant can keep their own ledger intact in their own account and ensure data integrity through high redundancy. In addition, it is difficult to achieve two payments or tampering because each participant retains their shared account book after the node unanimously agrees.

2.3.2 Methods for data consistency

In blockchain, nodes play a role in network routing, validation and propagation of block data, discovery of new nodes, and have the ability to dynamically change. The main methods to ensure data consistency in the network layer of blockchain systems are.

After generating new block data, other nodes in the entire network broadcast to verify it. The asymmetric Cryptography method is used to authenticate the transaction information of each node, and its identity information is authenticated. If confirmed to be correct, the processing results are recorded and a block is generated; If the verification is not passed, the transaction data can be deleted. Proof of work is carried out between various nodes in the network, thereby obtaining the right to conduct accounting treatment on them.

If a node obtains the account authority of the block, it can package the data to generate a new block, and broadcast the new block to the nodes of the whole network using the peer to peer communication protocol. The new data block is received by other nodes in the network and the work is verified. All record nodes are subjected to consistent confirmation of the data in the transaction records under the influence of the Merkel root value. When the verification passes, the node can receive the corresponding block. The verification of multiple transactions increases the computational cost of malicious node attacks and the difficulty of attacks, but improves the security of blockchain to ensure the consistency of blockchain data.

In a blockchain system, each node has a local data that can be added, deleted, and tampered with. This method utilizes a consensus mechanism to uniformly regulate each node to ensure consistent data backup between each node. On this basis, this article proposes a consensus mechanism based on the main chain node, which generates a new block as an outgoing node in the previous node or nodes. Due to the potential issues of malicious nodes and branching blocks in distributed networks, other nodes cannot directly add new blocks to their local blockchain after receiving them.

Through the consistent selection of main chain nodes, the correctness and consistency of data are guaranteed, and support is provided for the construction of trust relationships between untrusted entities. This protocol not only ensures the consistency and correctness of data in the blockchain system, but also lays a solid foundation for the fault-tolerant ability of the blockchain.

2.4 Privacy control and access rights management

2.4.1 Design of access trust architecture

In the design of access trust architecture, the big data platform is protected as a black box. When accessing the big data platform, the big data platform access trust technology based on dynamic and sustainable identity verification is adopted to accelerate the establishment of the security defense system of the big data platform and ensure the data security on the big data platform. Its core lies in strict identity authentication of the subject and object, and the construction of a unique, secure and reliable access control path, which fully reflects the security and controllability of the access process. The overall architecture constitutes a three-dimensional access trust architecture system through interface access control system, application access control, terminal agent perception, dynamic access trust control center, etc., achieving three security lines of data entry, data exit, and application interface. The owner of discretionary access control objects can be applied to small groups, because the permission management of small groups is relatively simple and stable, but it is not conducive to maintenance.

2.4.2 Improving data access control process

Based on the above design and social development background, it can be analyzed that in future work, in order to improve the importance of related control issues, it is necessary to optimize the integrity of data and control issues. These three formats are generally suitable for different situations. Therefore, in the specific use process, the corresponding access control methods should be selected based on the specific situation.

2.4.3 Backup and processing of important data

This article conducts eigenvalue analysis on the four core technologies commonly used in data processing through blockchain, and analyzes the functional applications of various technologies in data backup processing, as shown in Table 3.

Table 3
Characteristic values of various technologies for data backup processing

	Distributed ledger	Encryption algorithm	Smart contracts	Distributed storage
Data disaster recovery	5	8	4	7
Data re protection	5	9	6	6
Data extension	6	6	5	4
Data independence	2	3	2	8
Data access	9	1	8	8
Data compatibility	8	2	9	7

Currently, data backup processing can be applied to various types of network data. In some cases, when users lose valuable data in the cloud, they can easily perform relevant data operations by calling existing backup data. For network users, data backup can be used to upload information on the network, thereby avoiding losses. By establishing a virtual data platform architecture, potential information loss can be effectively avoided, providing effective protection for the transmission of data and information.

Based on the content in Table 3, this article analyzes the specific implementation steps of the professional data integration module under a better solution. When system users publish professional information and keywords, the professional information is distributed to the professional information resource evaluation module for review based on the technical field of intelligent keyword recognition. Subsequently, for expert information that has been reviewed, it is expanded according to keywords for easy retrieval and recommendation; In terms of extended label classification, association expansion is used to expand keywords from one or more combination rules in technical fields, technical problems, technical effects, uses, and synonyms through intelligent recognition programs. The expanded words are assigned a certain proportion of relevance to the original keywords, and are weighted according to the user’s selection.

3. Design and implementation of data privacy protection algorithms for mobile communications

With the development of cellular network mobile communication technology, big data has been widely used in information communication technology. Big data information communication technology continues to become the driving force of social development in the 21st century. However, with the development of technology, the leakage of personal information can also bring certain risks. Therefore, how to improve the privacy protection ability of personal information is a problem that deserves attention. Figure 3 shows the source statistics of privacy data breach of four communication interfaces tested.

Figure 3.

Source statistics of network node privacy data breach.

The x-axis in Fig. 3 is the source of privacy data breach, and the y-axis is the proportion. The legend is the serial number of the interface. Among them, in communication interface 1 and 2, the proportion of private data breach of wireless network was the largest. In communication interface 3, the proportion of private data breach of mobile data was the largest. In communication interface 4, the proportion of private data breach of location information was the largest. Therefore, among the seven privacy leakage sources mentioned above, in the study of cellular network mobile communication, it is important to focus on the sources with a larger proportion to protect user privacy and security.

3.1 Data encryption and verification

Figure 4 analyzes the encryption, decryption, and digital signature authentication processes. The user’s communication data is encrypted and the integrity and authenticity of the data are ensured through digital signature and verification mechanisms. Traditional symmetric encryption uses the same key for both parties, and if one party’s key is leaked, the entire communication can be cracked. Therefore, it is necessary to solve this problem.

Figure 4.

Encryption, decryption, and digital signature authentication process.

Figure 4 shows the authentication process of encryption, decryption, and digital signature in traditional asymmetric algorithms. The public key is used to encrypt plaintext and obtain ciphertext. The private key is used to decrypt the ciphertext and obtain plaintext. When using a digital signature, the ciphertext encrypted with a private key can only be decrypted using the corresponding public key, and there is no need to synchronize the key before communication. Therefore, asymmetric encryption and decryption mechanisms only require offline management of the private key.

3.2 Access control and permission management

On this basis, a data access control method based on intelligent contracts is proposed and used to authorize data. When a data user wishes to obtain a shared data, a request is issued to the endorsement node to include the hash value of the shared data. Through endorsement nodes, transactions are endorsed, and access to transactions is controlled through smart contracts. On this basis, a data sharing strategy based on smart contracts is proposed.

3.2.1 Static access control

Static access control is used to verify whether a user’s access complies with their preset access control policies. In the static access control method, the endorsement node obtains the user’s identity through the public key of the data requester, and obtains the access control policy of the file through the hash value of the shared data. If the corresponding access information is not obtained, it is considered as malicious access and return the access result that has been denied access. After obtaining the attribute information and access control policy information, a judgment is made on the attributes of the data requester. If it meets the access control rules, the static access control method can pass, while the dynamic access control method can continue. If the static access control method fails, it is considered an improper access method.

3.2.2 Dynamic access control

The dynamic access control method is based on the user’s access history stored in the blockchain, determines whether the user has malicious behavior, and punishes the user accordingly based on the actual malicious behavior. In the dynamic access control method, the endorsement node first obtains the historical record information of the user’s illegal access behavior, and uses it to determine whether the user has been punished. If so, it determines whether the punishment has been completed based on the current request time and the last illegal access time in the record. If it is still within the penalty period, the user’s access is denied.

When the user is not in the penalty period or has not been punished, it determines whether the user has frequently engaged in malicious behavior of illegal access by checking whether the number of illegal visits in the recent period has exceeded a threshold. If so, it is rejected. After verifying the illegal access behavior, it is also necessary to obtain the access history information of the user in the request file and determine it, which is similar to the logic for determining illegal access behavior. If the user has malicious intent, the user is given appropriate punishment time. Finally, based on the user’s access history, dynamic access control is implemented and the corresponding penalty time is given.

4. Mobile communication nodes under blockchain anonymity mechanism

Based on the above research, this study focused on the privacy protection mechanism of mobile communication data in cellular networks under blockchain, mainly focusing on three aspects: transaction latency, node throughput, and the reliability of blockchain anonymity mechanism on nodes under different task volumes.

The imtamability of blockchain can ensure the integrity and credibility of data, and increase the reliability of intelligent decisions. The decentralized nature of blockchain can reduce the participation of centralized institutions, improve the efficiency and security of data sharing, and the transparency of blockchain can increase the interpretability and Auditability of intelligent decisions.

Under the anonymous mechanism of blockchain, less latency results in more information and network communication in the shortest possible time. The latency of general data networks is less than 1 millisecond. There are many advantages to completing transactions in a short period of time, and the primary one is the ability to quickly obtain transaction information for optimal operations. Another possible advantage is the ability to detect and respond to market conditions more quickly, thus enabling more accurate responses. Here, the delay of transactions under different task volumes were analyzed, as shown in Fig. 5.

Figure 5.

Delay under different task volume transactions in the experiment.

The x-axis in Fig. 5 represents the task volume of 100–1000, and the y-axis represents the transaction delay time. The legend shows four experiments. In Experiment 1, the minimum delay time for trading was about 0.022ms when the task volume was 100, and the maximum delay time for trading was about 0.86 ms when the task volume was 900. In Experiment 2, the minimum delay time for trading was about 0.003 ms when the task volume was 100, and the maximum delay time for trading was about 0.72 ms when the task volume was 800. In Experiment 3, the minimum delay time for trading was about 0.27 ms when the task volume was 100, and the maximum delay time for trading was about 0.89 ms when the task volume was 900. In Experiment 4, the minimum delay time for trading was about 0.24 ms when the task volume was 900, and the maximum delay time for trading was about 0.97 ms when the task volume was 800. From the above data, it can be seen that the lowest delay occurred in the second experiment and the highest delay occurred in the fourth experiment, and changes in task volume can also affect the transaction delay to a certain extent.

Node throughput refers to the amount of data or transaction that a node can process per unit of time. Node throughput is crucial for the performance and scalability of mobile communication networks. Greater throughput can improve network processing efficiency, reduce transaction confirmation time, and improve user experience. Figure 6 tested the throughput of different nodes, with 10–100 nodes as the testing range. The number of failed and successful nodes tested was analyzed. The analysis is based on the failure rate and success rate, as shown in Fig. 6.

Figure 6.

Throughput analysis of different nodes.

Figure 6 consists of two groups of graphs, with the x-axis on the left as the node and the y-axis as the test result node. The legends show failure and success. The x-axis in the right figure represents the node, and the y-axis represents the node yield. The legend shows the failure rate and success rate. According to the values shown in Fig. 6, it can be concluded that when the nodes were 10–50, the maximum number of failed nodes tested was 4, while when the nodes were 60–100, the minimum number of failed nodes tested was 92. On the contrary, in the first 50 nodes, the maximum success rate of the tested nodes was 99%, and in the last 50 nodes, the maximum failure rate of the tested nodes was 96%.

Based on the previous research, this article studied the privacy protection of mobile communication data in cellular networks under blockchain, and analyzed the reliability of blockchain anonymity mechanism at nodes, as shown in Fig. 7.

Figure 7.

Reliability analysis of blockchain anonymity mechanism at nodes.

Figure 7 is composed of four groups of graphs, with the x-axis representing nodes and the y-axis representing reliability. The legends are for pseudonym, mixed currency, CryptoNote, and zero coin and zero currency, respectively. The figure shows the reliability of four blockchain anonymity mechanisms under the specified 6 nodes. Among them, the reliability range of the pseudonym mechanism in the specified nodes was between 91 and 99%, and the reliability range of the mixed currency mechanism in the specified nodes was between 91 and 96%. The reliability range of the CryptoNote and zero coin and zero currency mechanisms in the specified nodes was between 92 and 99%. It is not difficult to see that the reliability of the last two is slightly higher, but the average reliability of CryptoNote on six nodes was about 1.7% higher than that of the zero coin and zero currency protocol mechanisms. Therefore, this article further recommends CryptoNote as a research method for privacy protection of mobile communication data in cellular networks.

In summary, the above research showed that the transaction delay in the experimental test remained below 1 when the task volume was between 100 and 1000. Therefore, this experiment has provided some research direction. Next, by selecting a portion of nodes from the previous section, statistical analysis was conducted on the tested nodes based on failure and success. Finally, based on the four anonymity mechanisms under blockchain, which mechanism was more suitable for research was analyzed.

5. Conclusions

With the rapid development of cellular mobile communication technology, user data in the network has been greatly violated by privacy. Therefore, it is urgent to build a complete set of Internet privacy protection technology. This article studied the privacy protection algorithm for mobile communication data in cellular networks based on blockchain. In the design and implementation of mobile communication data privacy protection algorithms, the encryption, decryption, and digital signature identity verification processes were analyzed. In the experimental part, the reliability of several anonymous mechanisms in the blockchain for mobile communication data privacy protection in cellular networks was verified by analyzing the transaction latency and throughput at different nodes under different task volumes. The simulation experimental results show that this scheme can securely and effectively store data while protecting user privacy. Although the blockchain based cellular network mobile communication data privacy protection algorithm in this article has a certain guiding role for data security, there are still limitations in the research process. The implementation of privacy protection algorithms requires a large number of data samples to support, and in the experimental part, the sample size of this article is not sufficient, and privacy protection algorithms often require encryption or disturbance processing of communication data, This increases the complexity and latency of communication, and reduces the quality of service. In future research, in-depth research and improvement will be considered from the perspectives of data sample size and algorithm service quality, in order to promote the healthy development of cellular network mobile communication.

Data availability

Data is available upon reasonable request.

Funding

This work was supported by the Special Fund of Advantageous and Characteristic disciplines(Group) of Hubei Province. This work was supported by the Guiding project of Scientific Research Plan of Education Department of Hubei Province B2022338.

Footnotes

Conflict of interest

The authors have stated explicitly that there are no conflicts of interest in connection with this article.

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Blockchain based mobile communication data privacy protection algorithm for cellular networks

Abstract

Keywords

1. Introduction

2.1 Overall architecture of blockchain

Table 1 Score of blockchain consensus layer mechanism

Table 2 Performance comparison of blockchain anonymous technology

2.3.1 Data consistency assurance

2.3.2 Methods for data consistency

2.4 Privacy control and access rights management

2.4.1 Design of access trust architecture

2.4.2 Improving data access control process

2.4.3 Backup and processing of important data

Table 3 Characteristic values of various technologies for data backup processing

3.2.1 Static access control

3.2.2 Dynamic access control

4. Mobile communication nodes under blockchain anonymity mechanism

Data availability

Funding

Footnotes

Conflict of interest

References

Table 1
Score of blockchain consensus layer mechanism

Table 2
Performance comparison of blockchain anonymous technology

Table 3
Characteristic values of various technologies for data backup processing