School of Intelligent Manufacturing and Information, Jiangsu Shipping College, Nantong, Jiangsu 226010, China.
Comput Intell Neurosci. 2022 Jun 28;2022:1926794. doi: 10.1155/2022/1926794. eCollection 2022.
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.
信息技术的快速迭代使得在线社交网络的发展越来越迅速，其相应的网络规模也越来越大、越来越复杂。相应的用于处理社交网络及其相关问题的算法也在不断增加。相应的隐私保护算法，如加密算法、访问控制策略算法和差分隐私保护算法等都已经被研究和分析，但这些算法并没有完全解决隐私泄露的问题。基于此，本文首先基于深度卷积神经网络算法对在线社交网络的相关信息和内容进行搜索和准确过滤，从而实现对用户安全内容的感知和保护。对于相应的图形和数据，本文引入压缩感知技术对相应的图形和数据进行随机干扰。在跟踪网络信息泄露算法的层面上，本文提出了一种基于数字指纹的网络信息泄露跟踪算法，主要利用相关插件实现用户的唯一识别处理，利用数字指纹的唯一性实现对泄露者的跟踪处理，并根据社交网络拓扑结构制定相应的编码方案，同时，本文提出的网络信息泄露跟踪算法在相应的数字编码效率和可扩展性方面也具有高效率。为了验证基于深度学习的在线社交网络信息泄露跟踪算法的优势，本文将其与传统算法进行了比较。在实验部分，本文主要比较了准确率指标、召回率指标和性能指标。在相应的准确率指标水平上，可以看出本文提出的算法与传统算法相比最大提高了约 10%。在相应的召回率指标水平上，提出的算法比传统算法高出约 5-8%。对应于整体性能指标，与传统算法相比，它的性能提高了约 50%。比较结果表明，提出的算法具有更高的准确性，相应的源跟踪更准确。
