With the growing use of social networks and the consequent rise in the sharing of personal information online, privacy has become a major concern, leading to an increased demand for efficient anonymization techniques. This research proposes innovative methods for hiding information in weighted social network graphs. We provide a topology-modification technique for precisely hiding important nodes in the user network. We also present a differential privacy-based method to safeguard edge weights while taking weight topology correlations into account. With accuracy rates of 87.04% and 94.73%, respectively, our approaches are highly effective in connections to significant nodes and concealing edge weights, respectively. Our methods, which safeguard data integrity to a larger extent than earlier techniques, alter the original graph as little as possible (only 12% of the original edges are changed), as measured by well-known graph metrics. Experiment results on the extracted Instagram posts show that our solutions outperform current approaches in terms of privacy and preserved usefulness.

中文翻译：

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保护你的社交圈：保护关键联系和边缘重要性的策略

随着社交网络的使用日益广泛以及随之而来的在线个人信息共享的增加，隐私已成为一个主要问题，导致对高效匿名技术的需求不断增加。这项研究提出了在加权社交网络图中隐藏信息的创新方法。我们提供了一种拓扑修改技术，用于精确隐藏用户网络中的重要节点。我们还提出了一种基于差分隐私的方法来保护边缘权重，同时考虑权重拓扑相关性。我们的方法的准确率分别为 87.04% 和 94.73%，在连接重要节点和隐藏边权重方面非常有效。我们的方法比早期技术更大程度地保护数据完整性，尽可能少地改变原始图（仅改变 12% 的原始边），根据众所周知的图指标进行测量。对提取的 Instagram 帖子的实验结果表明，我们的解决方案在隐私和保留有用性方面优于当前的方法。