基于进化扰动的目标防御链接预测攻击
Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations
摘要：
在社交网络中，通过删除一些目标敏感链接，可以实现隐私保护。然而，在可观测网络上，通过链接预测方法仍然可以重新观测到一些隐藏链接。本文采用传统的链接预测方法资源分配指数（RA）进行隐私攻击。提出了几种防御方法，包括启发式和进化的方法，以保护目标链路从RA攻击通过进化扰动。这是第一次研究针对链接预测攻击的隐私保护。从网络中随机选择一些链接作为实验的目标链接。在六个真实网络上的仿真结果表明了进化扰动方法在目标防御RA攻击中的优越性。此外，传输实验表明，虽然进化扰动方法是针对RA攻击而设计的，但它对其他基于链接预测的攻击也是有效的。
英文摘要：
In social networks, by removing some target-sensitive links, privacy protection might be achieved. However, some hidden links can still be re-observed by link prediction methods on observable networks. In this paper, the conventional link prediction method named Resource Allocation Index (RA) is adopted for privacy attacks. Several defense methods are proposed, including heuristic and evolutionary approaches, to protect targeted links from RA attacks via evolutionary perturbations. This is the first time to study privacy protection on targeted links against link-prediction-based attacks. Some links are randomly selected from the network as targeted links for experimentation. The simulation results on six real-world networks demonstrate the superiority of the evolutionary perturbation approach for target defense against RA attacks. Moreover, transferring experiments show that, although the evolutionary perturbation approach is designed to against RA attacks, it is also effective against other link-prediction-based attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
