[Retracted] E-Commerce Data Access Control and Encrypted Storage Based on Internet of Things

He, Yue; Hu, Wanda

doi:https://doi.org/10.1155/2022/4547002

Mathematical Problems in Engineering

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Abstract Introduction Literature Review Analysis of Results Conclusion Data Availability Conflicts of Interest References Copyright Related Articles

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Research Article | Open Access

Volume 2022 | Article ID 4547002 | https://doi.org/10.1155/2022/4547002

[Retracted] E-Commerce Data Access Control and Encrypted Storage Based on Internet of Things

Yue He¹and Wanda Hu¹

Academic Editor: Hengchang Jing

Received15 Jun 2022

Accepted26 Jul 2022

Published19 Sept 2022

Abstract

In order to discuss the access control and encrypted storage of Internet of Things data for e-commerce data, a method of Internet of Things data access control and encrypted storage based on optical fiber network communication is proposed. Build an IoT application model based on optical fiber network communication, which consists of a perception layer, a network layer, and an application layer. The network layer is through the optical fiber communication network. The information collected by the optical fiber sensor in the perception layer is transmitted to the application layer for processing. And the model-aware layer adopts the dynamic transformation encryption method for attribute decomposition, and the Internet of Things data access control strategy based on key management and implement IoT data access control and encrypted storage based on the IoT application model of optical fiber network communication. The experimental results show that when this method implements access control and encrypted storage of IoT data, it has the advantages of high efficiency, low storage space overhead, and low computational cost of fiber network nodes, and the security of the processed IoT data is as high as 97.77%. Conclusion. This method can effectively deal with the limited computing performance and storage performance of the perception layer nodes of the application model; larger and larger application requirements with a large number of nodes.

1. Introduction

With the emergence and development of new computing technologies such as cloud computing, Internet of Things, and big data, the informatization of the world has led to a deep reform of information transmission technology on a global scale, at every level of national upgrading, and social development. The demand for information technology has also reached a height that has never been reached [1]. Build a global information sharing mechanism. It is of key significance to improve international economic, technological, educational cooperation, and cultural exchanges. Information security issues cover every corner of society such as finance, medical care, and electricity. There are non-singularity of attack forms, diversity of threats, large scope of influence, and strong suddenness. Security issues in information networks have become an explosive drawback in the development of informatization in various countries around the world [2]. In the big data environment, information security has become a key issue in the field of information security. With the development of Internet technology, e-commerce has gradually become popular in daily life, which not only reduces the cost of business, but also drives the rapid development of the logistics industry. In the process of e-commerce transactions, some user privacy data is easily leaked, posing a threat to user privacy security. Therefore, it is necessary to use access control and encrypted storage technology and privacy protection of e-commerce data to ensure that users’ privacy is not leaked as shown in Figure 1 [3]. By adopting the method of joint network feature analysis, the feature extraction and statistical analysis of e-commerce group user access data in the era of big data is realized, and the level of information management and e-commerce information access is improved. However, in the process of statistical analysis of user access to e-commerce groups, there is no in-depth analysis of user data access and encrypted storage. It is very easy to have information leakage problems.

2. Literature Review

In the early stage of the development of the Internet of Things, some researches put forward the data processing scheme of the Internet of Things based on cloud computing. Cloud-centric IoT usually consists of three layers, namely, cloud server layer, IoT device layer, and application layer [4]. The device layer and the application layer are separated, the device is the edge component of the IoT network, and all devices are identified, authenticated, and connected through the cloud server, abstract IoT edge components into virtualized resources, take the system from a device-centric single device transaction, and shift to IoT services and applications that simultaneously provide a large number of device transactions. Shi et al. proposed a data storage framework based on cloud computing, the framework combines and extends multiple existing databases in order to store and manage various types of IoT data, the disadvantage of this framework is the long waiting time, which is also an inherent property of cloud storage schemes [5]. Dhiman et al. proposed a three-tier IoT data storage framework with front-end-middleware-backend, which can be seamlessly integrated into existing enterprise information systems, however, this scheme has low storage efficiency for heterogeneous data [6]. In order to store large amounts of heterogeneous data, Bradha et al. propose a hybrid structure- and object-oriented approach to optimize data storage and retrieval [7]. Sun et al. proposed a method to realize data communication from cloud to IoT devices based on buffer and communication link-state, in order to improve the overall performance [8]. Nie et al. solved the problem of uncontrolled malicious modification under cloud platform that may destroy the availability of shared data, a public audit solution is proposed and the identity privacy and identity traceability of group members can be protected at the same time [9]. However, cloud computing does not support the processing and storage of low-latency sensitive data, and because cloud data allows multiple authorized users to access, it cannot provide sufficient evidence for the whereabouts of data information and the operation history of subjects at all levels. Therefore, some special fields cannot be satisfied (such as industrial control systems and traceability systems) the need to audit the access process of system dynamic data. Once a problem occurs, it is difficult to determine the responsibility. The Internet of Things technology can establish a collection between users and permissions and monitor access requests in the process of data use in real-time, which is conducive to dynamic access and access control. Therefore, the authors introduce IoT technology into e-commerce data access control and encrypted storage with the advantage of IoT real-time monitoring of data access requests. The authors study e-commerce data access control and encrypted storage technology based on IoT in order to further protect the privacy of e-commerce data.

3. Research Methods

3.1. Building an E-Commerce Data Sharing Model

In order to realize the access control and encrypted storage of e-commerce data based on the Internet of Things, first of all, establish an e-commerce data security sharing model, preset data attribute collection, and verify that user access meets the requirements. This ensures data sharing security. After the data is encrypted, the key may be lost and leaked during the key management process. In order to solve this problem, a sharing model is established based on user attributes. Different attribute set users obtain different private keys [10]. Therefore, each private key will not reveal the core information, reducing the risk of leakage. The restored complete e-commerce data is obtained by combining multiple authorized user sets. If only some of the user attribute keys are known, the original e-commerce data cannot be restored, which improves the security of data sharing to a certain extent.

Because IoT applications based on optical fiber network communication are mostly developed around a certain industry problem, an IoT application model based on optical fiber network communication is constructed. The details of the IoT application model based on optical fiber network communication are shown in Figure 2. In Figure 2, the perception layer is based on the data collected by optical fiber sensors. The network layer is based on the optical fiber communication network to transmit the information obtained by the perception layer to the application layer for processing. The difference between this method and the broadband transmission method is that the former can complete long-distance transmission of information [11, 12]. There are several common application support sub-layer technologies and application platforms in the application layer and complete the high integration of network technology.

3.2. Dynamic Transformation Encryption Based on Attribute Decomposition

Because the number of cloud data storage in the IoT application model based on optical fiber network communication is very large, if the IoT data attributes are decomposed and encrypted for each data relationship, different keys will appear. If each key is saved, it will increase the storage space, use dynamic encryption methods, and dynamically generate keys. It is used to reduce the storage space of cloud data and improve data security [13]. When generating keys dynamically, the probability of duplicate keys must be close to 0, even if the elements have shared plaintext, the difference in position will generate differential ciphertext, and the dynamic generation of ciphertext is difficult. For element , if , then , conversely, , then set , then stop at , where x represents a single attribute. If and correspond, take whichever is used for encryption calculation.

Assuming that the IoT data attribute record code j in is known, then its key , this is the first dynamic transformation, representing XOR calculation, and describes the balance coefficient [14]. Again the dynamic transformation key is , then

Among them, represents the positive attribute decomposition of element . For a random data relationship set O, through the minimum encryption strength attribute decomposition, the Internet of Things data attribute set that conforms to the privacy constraint rules is generated, for the finite element group decomposed by positive attribute , its element is . Assuming that is numerical data, the value range is [−b, b], then . Here, the original value and non-regular transformation are used. It can realize dynamic encrypted storage [15].

3.3. IoT Data Access Control Strategy Based on Key Management

Because in the IoT application model based on optical fiber network communication, the types of data collected by optical fiber sensors in the perception layer are generally only allowed to access or deny access to users. Therefore, previous access control methods are not suitable for IoT environments. However, different types of information have different access levels, so that users can use the keys set in section 3.2 to access data at different levels according to the model, high-level users can not only access data at their own level, but also access data at different levels, it is also possible to obtain access rights for low-level users based on key derivation to access more resources [16]. Hierarchical access control mechanisms can be accomplished using key management methods.

3.4. Scheme Description

After the IoT application model based on optical fiber network communication is initialized, each level node corresponds to a unique identifier and a key , is used to protect this layer of resources, then and identify public information release, and set the topological structure of the directed acyclic graph B as public information release, then release . In order to obtain the information resources corresponding to , the user must obtain the valid key issued by the authorization center, and then perform the decryption operation to realize the access to the corresponding resources of the hierarchical nodes [17].

Based on theoretical analysis, the user must obtain a when accessing an , and perform decryption processing, with the increase of the access level, when the user extracts the key multiple times, not only does it cause communication burdens and threats to the perceptual layer network accessed, but also users will suffer from insufficient storage performance and long-distance communication performance, the problem of being attacked by an adversary. Evaluate the partial order relationship between neighboring nodes in the directed acyclic graph, so that the user can obtain the corresponding key of the upper node , the lower-level key can be obtained by designing a suitable algorithm, and according to this principle, the keys of all nodes in the directed tree with as the root can be obtained.

Based on the access control strategy proposed by the authors, taking into account the security status of the user and the hierarchical node , only the key material that can obtain is stored. In this way, the non-public information corresponding to the network node is the key material , and the corresponding public information is ; becomes two keys after operation, the key is used to ensure the security of the self-layer resource , and the keys and obtain the lower-layer key material by the designed key acquisition method. In this way, the key for accessing the resources of this layer is obtained. If there are still lower-level nodes that need to be accessed, this method is used cyclically to obtain the corresponding key material, where the numbers of and indicate the release of public information; represents private information and saves it; the purpose of the strategy is to use upper layers and , according to the key acquisition method, the lower-layer can be obtained safely and quickly.

3.5. Strategy Building

(1)Initialize. Set the directed acyclic graph and security parameters ; among them, X represents the node set of B, and Y represents the set of directed edges. The initialization process of the access control policy is as follows:(i)Step 1: Select each node in graph B, assign a unique identifier , at the same time, the corresponding key material is arbitrarily selected. Use and to operate and operate , where , both represent random values [18, 19].(ii)Step 2: Select each directed edge , , and operate in graph B.(iii)Step 3: Set the acquired as public information release by operation, as private information for storage, and stop after initialization.(2)Key acquisition method. If a user accesses the network resources of the perception layer, it must obtain the key material of the corresponding layer. Set graph B, hierarchical nodes u, , among which , the user has obtained the key material of the corresponding u and the public information in graph B [20]. The purpose of the user is to use the designed key acquisition method based on the mastered key material and public information, in order to obtain the key material of the hierarchical node u and to operate the key material of access in turn, so as to calculate the key to access . This key acquisition method does not need to analyze the detailed hierarchical structure of the perception network, the user only controls the of the source layer node, and the intermediate result obtained does not have the key for each layer of protection resources, which can reduce the security risk of the method. Furthermore, this method is not limited to tree-like hierarchies. Assuming that the user has obtained the -related key material , and the object is to access the hierarchical node resource, then obtain the , then the user can obtain the by using the 2-round key acquisition method to achieve access.

4. Analysis of Results

In the experiment, the efficiency of the three programs of encryption, key acquisition, and decryption is tested by increasing the number of IoT data attributes in the IoT application model based on optical fiber network communication and expanding the scale of the access structure and comparing the performance of the method in this paper, the cloud storage data access control method based on CP-ABE algorithm, and the multi-privilege secure cloud storage access control method based on CP-ABE and XACML. The processor used in the experiment is set to Inter 2. 3 GHz CPU, the memory is 9 GB, the virtual machine is VMware Workstation 6. 5. 2, and the performance test software is Ubuntu 10. 11. In the experiment, the number of IoT data attributes of the IoT application model based on optical fiber network communication was increased from 0 to 8 000, the test results of the three programs are shown in Figure 3. Figure 3(a) shows the time consumption result of encryption, Figure 3(b) shows the time consumption result of decryption, and Figure 3(c) shows the time consumption result of key acquisition. According to Figure 3, the proposed method takes time in the whole process with the IoT application model based on optical fiber network communication, and the number of IoT data attributes increases, the time-consuming access control methods of cloud storage data based on the CP-ABE algorithm and multiauthority secure cloud storage based on CP-ABE and XACML also significantly increase with the increase of the number of IOT data attributes in the IOT application model. However, among the three methods, the method proposed in this paper is always the least time-consuming when applied to the encrypted storage of IOT data [21].

(a)

(b)

(c)

Figure 4 shows the test results of cloud data storage space under the control of three methods. According to Figure 4, it can be seen that, 3 ways to save space, with the increase in the number of users of the Internet of Things application model based on optical fiber network communication. Among them, the increase rate in the former stage is slower, and the increase rate in the latter stage is faster [22]. When the number of users is as high as 800, when the three methods deal with the problems of IoT data access control and encrypted storage, the storage space occupied is 16 MB, 33 MB, and 61 MB, compared with the other two methods, the proposed method has obvious advantages and can store the key information corresponding to the data more closely, so there is a small storage space overhead.

The data security test results of the IOT application model show that the data security is as high as 97.77% after being processed by the method proposed in this paper, while the maximum data security of the IOT application model processed by the cloud storage data access control method using the CP-ABE algorithm and the cloud storage access control method using CP-ABE and XACML multiauthority security are 85.01% and 77.98%, respectively. Under the proposed method, the IoT application model based on optical fiber network communication has the highest IoT data security.

For the three methods of statistical data of the IOT application model, the calculation amount of optical network nodes when they implement access control and encrypted storage is compared, and the comparison results are shown in Table 1. In Table 1, the proposed method for the Internet of Things application model based on optical fiber network communication, when the Internet of Things data implements access control and encrypted storage, the maximum amount of network node calculation is 10.24 MB, 10.45 MB, and 10.45 MB, and less than the other two methods; the proposed method can reduce the calculation amount of fiber network nodes when implementing access control and encrypted storage of IoT data.

5. Conclusion

The computing performance and storage performance of the sensing layer nodes of the IoT application model based on optical fiber network communication are limited, and the number of nodes is large, which increases with the increase of application requirements, a method of IoT data access control and encrypted storage based on optical fiber network communication is proposed, this problem is dealt with and its effectiveness is verified in experiments. The conclusions obtained from the experiments are as follows:(1)The proposed method is always the least time-consuming in the three procedures of encryption, key acquisition, and decryption and can complete the encrypted storage of IoT data in the shortest time.(2)When the number of users is as high as 800 and when the three methods deal with the problems of IoT data access control and encrypted storage, the storage space occupied is 16 MB, 33 MB, and 61 MB, and the storage space overhead of the proposed method is the smallest.(3)The security of IoT data after the proposed method is as high as 97.77%.(4)When the proposed method implements access control and encrypted storage of IoT data, the maximum amount of network node computation is 10.24 MB, 10.45 MB, and 10.45 MB, compared with other methods, when the proposed method implements access control and encrypted storage of IoT data, it can reduce the computational load of fiber network nodes.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Copyright

Copyright © 2022 Yue He and Wanda Hu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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