Unlike IPv4 addresses, which are typically masked by a NAT, IPv6 addresses
could easily be correlated with user activity, endangering their privacy.
Mitigations to address this privacy concern have been deployed, making existing
approaches for address-to-user correlation unreliable. This work demonstrates
that an adversary could still correlate IPv6 addresses with users accurately,
even with these protection mechanisms. To do this, we propose an IPv6 address
correlation model - SiamHAN. The model uses a Siamese Heterogeneous Graph
Attention Network to measure whether two IPv6 client addresses belong to the
same user even if the user's traffic is protected by TLS encryption. Using a
large real-world dataset, we show that, for the tasks of tracking target users
and discovering unique users, the state-of-the-art techniques could achieve
only 85% and 60% accuracy, respectively. However, SiamHAN exhibits 99% and 88%
accuracy.

该论文展示了一种基于 IPv6 地址相关性模型 - SiamHAN 的方法，即使在受到 TLS 加密保护的情况下，对于追踪目标用户和发现独特用户的任务，其准确性分别为 99％和 88％。