Kuang Linghong, Si Kunliang, Zhang Jing
School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, Fujian, China.
PeerJ Comput Sci. 2024 Sep 18;10:e2244. doi: 10.7717/peerj-cs.2244. eCollection 2024.
A social network is a platform that users can share data through the internet. With the ever-increasing intertwining of social networks and daily existence, the accumulation of personal privacy information is steadily mounting. However, the exposure of such data could lead to disastrous consequences. To mitigate this problem, an anonymous group structure algorithm based on community structure is proposed in this article. At first, a privacy protection scheme model is designed, which can be adjusted dynamically according to the network size and user demand. Secondly, based on the community characteristics, the concept of fuzzy subordinate degree is introduced, then three kinds of community structure mining algorithms are designed: the fuzzy subordinate degree-based algorithm, the improved Kernighan-Lin algorithm, and the enhanced label propagation algorithm. At last, according to the level of privacy, different anonymous graph construction algorithms based on community structure are designed. Furthermore, the simulation experiments show that the three methods of community division can divide the network community effectively. They can be utilized at different privacy levels. In addition, the scheme can satisfy the privacy requirement with minor changes.
社交网络是一个用户可以通过互联网共享数据的平台。随着社交网络与日常生活的交织日益紧密，个人隐私信息的积累也在不断增加。然而，此类数据的泄露可能会导致灾难性后果。为缓解这一问题，本文提出了一种基于社区结构的匿名群组结构算法。首先，设计了一种隐私保护方案模型，该模型可根据网络规模和用户需求进行动态调整。其次，基于社区特征引入模糊隶属度概念，然后设计了三种社区结构挖掘算法：基于模糊隶属度的算法、改进的Kernighan-Lin算法和增强的标签传播算法。最后，根据隐私级别，设计了基于社区结构的不同匿名图构建算法。此外，仿真实验表明，这三种社区划分方法能够有效地划分网络社区。它们可在不同隐私级别使用。此外，该方案只需进行微小改动就能满足隐私需求。
