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. 2022 Jun 28:2022:1926794.
doi: 10.1155/2022/1926794. eCollection 2022.

Research on Online Social Network Information Leakage-Tracking Algorithm Based on Deep Learning

Shuhe Han  1
Affiliations

Research on Online Social Network Information Leakage-Tracking Algorithm Based on Deep Learning

Shuhe Han. Comput Intell Neurosci. .

Abstract

The rapid iteration of information technology makes the development of online social networks increasingly rapid, and its corresponding network scale is also increasingly large and complex. The corresponding algorithms to deal with social networks and their corresponding related problems are also increasing. The corresponding privacy protection algorithms such as encryption algorithm, access control strategy algorithm, and differential privacy protection algorithm have been studied and analyzed, but these algorithms do not completely solve the problem of privacy disclosure. Based on this, this article first searches and accurately filters the relevant information and content of online social networks based on the deep convolution neural network algorithm, so as to realize the perception and protection of users' safe content. For the corresponding graphics and data, this article introduces the compressed sensing technology to randomly disturb the corresponding graphics and data. At the level of tracking network information leakage algorithm, this article proposes a network information leakage-tracking algorithm based on digital fingerprint, which mainly uses relevant plug-ins to realize the unique identification processing of users, uses the uniqueness of digital fingerprint to realize the tracking processing of leakers, and formulates the corresponding coding scheme based on the social network topology, and at the same time, the network information leakage-tracking algorithm proposed in this article also has high efficiency in the corresponding digital coding efficiency and scalability. In order to verify the advantages of the online social network information leakage-tracking algorithm based on deep learning, this article compares it with the traditional algorithm. In the experimental part, this article mainly compares the accuracy index, recall index, and performance index. At the corresponding accuracy index level, it can be seen that the maximum improvement of the algorithm proposed in this article is about 10% compared with the traditional algorithm. At the corresponding recall index level, the proposed algorithm is about 5-8% higher than the traditional algorithm. Corresponding to the overall performance index, it improves the performance by about 50% compared with the traditional algorithm. The comparison results show that the proposed algorithm has higher accuracy and the corresponding source tracking is more accurate.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Figures

Figure 1
Figure 1
Architecture of online social network information disclosure tracking algorithm based on deep learning.
Figure 2
Figure 2
Architecture of the information network privacy protection algorithm based on the deep convolution neural network algorithm.
Figure 3
Figure 3
Framework diagram of personal information disclosure model of online social network users.
Figure 4
Figure 4
Digital fingerprint system model block diagram of the online social network.
Figure 5
Figure 5
Digital fingerprint generation index diagram of the online social network.
Figure 6
Figure 6
Verification flowchart of the information network privacy protection algorithm based on the deep convolution neural network algorithm.
Figure 7
Figure 7
Network structure accuracy test curve.
Figure 8
Figure 8
Accuracy and recall curve of information network privacy protection algorithm based on the deep convolution neural network algorithm.
Figure 9
Figure 9
Performance comparison between information network privacy protection algorithm based on the deep convolution neural network algorithm and traditional algorithm.
See this image and copyright information in PMC

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