While privacy-focused browsers have taken steps to block third-party cookies
and browser fingerprinting, novel tracking methods that bypass existing
defenses continue to emerge. Since trackers need to exfiltrate information from
the client- to server-side through link decoration regardless of the tracking
technique they employ, a promising orthogonal approach is to detect and
sanitize tracking information in decorated links. We present PURL, a
machine-learning approach that leverages a cross-layer graph representation of
webpage execution to safely and effectively sanitize link decoration. Our
evaluation shows that PURL significantly outperforms existing countermeasures
in terms of accuracy and reducing website breakage while being robust to common
evasion techniques. We use PURL to perform a measurement study on top-million
websites. We find that link decorations are widely abused by well-known
advertisers and trackers to exfiltrate user information collected from browser
storage, email addresses, and scripts involved in fingerprinting.

通过使用跨层图表示网页执行的机器学习方法，PURL 安全有效地清理链接装饰，显著优于现有对策并抵御常见的逃避技术，从而在隐私保护浏览器中阻止追踪方法，并发现了链接装饰被知名广告商和追踪器滥用以窃取用户信息的现象。

PURL: 链接修饰的安全有效消毒

PURL: Safe and Effective Sanitization of Link Decoration

This paper proposes PuRL - a deep reinforcement learning (RL) based algorithm
for pruning neural networks. Unlike current RL based model compression
approaches where feedback is given only at the end of each episode to the
agent, PuRL provides rewards at every pruning step. This enables PuRL to
achieve sparsity and accuracy comparable to current state-of-the-art methods,
while having a much shorter training cycle. PuRL achieves more than 80%
sparsity on the ResNet-50 model while retaining a Top-1 accuracy of 75.37% on
the ImageNet dataset. Through our experiments we show that PuRL is also able to
sparsify already efficient architectures like MobileNet-V2. In addition to
performance characterisation experiments, we also provide a discussion and
analysis of the various RL design choices that went into the tuning of the
Markov Decision Process underlying PuRL. Lastly, we point out that PuRL is
simple to use and can be easily adapted for various architectures.

本文提出了 PuRL，一种基于深度强化学习（RL）的神经网络剪枝算法，通过在每个剪枝步骤上提供奖励，可以在短时间内达到与现有最先进方法相当的稀疏性和准确性。 PuRL 在 ResNet-50 模型上实现了超过 80％ 的稀疏性，并在 ImageNet 数据集上保持了 75.37％ 的 Top-1 精度，并且可以很容易地适应于各种体系结构。