Investigating Traffic Analysis Attacks on Apple iCloud Private Relay
Investigating Traffic Analysis Attacks on Apple iCloud Private Relay
复制标题
调查针对 Apple iCloud 专用中继的流量分析攻击
DOI：
10.1145/3579856.3595793
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Houmansadr, Amir
中科院分区：
文献类型：
--
作者：
Zohaib, Ali;Sheffey, Jade;Houmansadr, Amir
iCloud Private Relay（PR）是Apple于2021年6月推出的一项新功能，旨在通过保护一部分网络流量免受本地窃听者和使用基于IP的跟踪的网站的攻击，来增强在线隐私。该服务集成到苹果最新的操作系统中，并使用两跳架构，用户的网络流量通过两个由不相交实体运行的代理进行中继。PR的多跳架构类似于传统的匿名系统，如Tor和混合网络。然而，已知这样的系统容易受到被称为流量分析的漏洞的影响：拦截对手（例如，恶意路由器）可以试图通过分析特性（例如，分组定时和大小）。特别是，以前的工作已经广泛研究了Tor对网站指纹和流量相关性的敏感性，这两种主要的流量分析形式。在这项工作中，我们首次研究了流量分析对最近引入的PR的威胁。首先，我们探索PR的当前架构，建立针对PR的流量分析攻击的全面威胁模型。其次，我们通过对互联网路由的经验测量，评估现实世界AS级对手施加的风险，量化这些针对PR的攻击的潜在可能性。我们的评估表明，一些自治系统在一个特别强大的位置，以执行流量分析的大部分PR流量。最后，在证明了这些攻击发生的可能性之后，我们评估了几种流量相关性和网站指纹攻击在PR流量上的性能。我们的评估表明，PR非常容易受到最先进的网站指纹和流相关攻击，这两种攻击都取得了很高的成功率。我们希望我们的研究能够阐明流量分析对当前公关部署的重要性，说服苹果进行设计调整以减轻风险。
The iCloud Private Relay (PR) is a new feature introduced by Apple in June 2021 that aims to enhance online privacy by protecting a subset of web traffic from both local eavesdroppers and websites that use IP-based tracking. The service is integrated into Apple’s latest operating systems and uses a two-hop architecture where a user’s web traffic is relayed through two proxies run by disjoint entities.PR’s multi-hop architecture resembles traditional anonymity systems such as Tor and mix networks. Such systems, however, are known to be susceptible to a vulnerability known as traffic analysis: an intercepting adversary (e.g., a malicious router) can attempt to compromise the privacy promises of such systems by analyzing characteristics (e.g., packet timings and sizes) of their network traffic. In particular, previous works have widely studied the susceptibility of Tor to website fingerprinting and flow correlation, two major forms of traffic analysis.In this work, we are the first to investigate the threat of traffic analysis against the recently introduced PR. First, we explore PR’s current architecture to establish a comprehensive threat model of traffic analysis attacks against PR. Second, we quantify the potential likelihood of these attacks against PR by evaluating the risks imposed by real-world AS-level adversaries through empirical measurement of Internet routes. Our evaluations show that some autonomous systems are in a particularly strong position to perform traffic analysis on a large fraction of PR traffic. Finally, having demonstrated the potential for these attacks to occur, we evaluate the performance of several flow correlation and website fingerprinting attacks over PR traffic. Our evaluations show that PR is highly vulnerable to state-of-the-art website fingerprinting and flow correlation attacks, with both attacks achieving high success rates. We hope that our study will shed light on the significance of traffic analysis to the current PR deployment, convincing Apple to perform design adjustments to alleviate the risks.
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