Streams of data have become the ubiquitous communication model on today's Internet. For strong anonymous communication, this conflicts with the traditional notion of single, independent messages, as assumed e.g. by many mixnet designs. In this work, we investigate the anonymity factors that are inherent to stream communication. We introduce Progressive Pruning}, a methodology suitable for estimating the anonymity level of streams. By mimicking an intersection attack, it captures the susceptibility of streams against traffic analysis attacks. We apply it to simulations of tailored examples of stream communication as well as to large-scale simulations of Tor using our novel TorFS simulator, finding that the stream length, the number of users, and how streams are distributed over the network have interdependent impacts on anonymity. Our work draws attention to challenges that need to be solved in order to provide strong anonymity for stream-based communication in the future.

翻译：数据流已成为当今互联网上无处不在的通信模型。对于强匿名通信而言，这与传统上对独立单条消息的假设（例如许多混洗网络设计所基于的假设）存在冲突。在本研究中，我们探究了流通信固有的匿名性影响因素。我们提出了渐进式剪枝，这是一种适用于评估流匿名性水平的方法论。该方法通过模拟交集攻击，捕捉流对流量分析攻击的脆弱性。我们将其应用于定制化流通信示例的仿真，以及使用我们新型的TorFS模拟器进行的大规模Tor网络仿真，发现流长度、用户数量以及流在网络中的分布方式对匿名性存在相互依赖的影响。本研究揭示了未来为基于流的通信提供强匿名性所需解决的关键挑战。