Feedback and Optimisation for Well-behaved Anonymous Communication Networks

匿名通信网络（ACNs），如Tor和混合网络，保护我们敏感的通信元数据，例如我们与谁交谈，我们多久聊天一次以及多长时间。这种元数据是隐私敏感的，因为它可以用来揭示可能被隐藏的秘密，即使使用端到端加密。这个项目是及时的，因为自从斯诺登揭露国家级大规模监控以来，互联网上对通信隐私的主流兴趣已经增长。然而，这是一个挑战，因为它是很难调整系统参数，以匹配实际实现的隐私级别，这是由于事实上，ACN的隐私，安全和性能受到环境条件和用户行为的严重影响。例如，人们通常认为消息在网络上流动时不会被分割成更小的片段。然而，现实情况是，由于性能原因，消息经常被分解。同样，假设有一个恒定的用户活动水平，然而，用户往往有一个昼夜活动周期与突发和平静整个day.To补救这种现状，这个项目的目的是弥合根本差距，并提供了一个框架和一套方法来测量，分析，并调整在现实环境中的ACNs。它通过追求三个目标来做到这一点：1。映射和调整：新的分析，以揭示和形式化抽象的安全参数和现实世界的网络测量之间的关系，以期最佳地调整ACN。2.反馈：研究具有反馈回路的新型ACN设计，这些反馈回路能够在运行时自动调整安全参数。3.用例验证：在电子邮件、Web浏览和物联网数据收集系统的目标用例中进行评估，以验证自动调优方法。让我们考虑一个电子邮件提供商希望提供用户匿名作为市场差异化。参考电子邮件安全混合网络文献中的最新技术，对于非专家来说，很难推理如何正确地为电子邮件提供商的特定用户群参数化混合网络。映射和调优是阻碍部署的缺失因素。在启动时给定一个调优的ACN，可以使用反馈来自动设置和调整运行时电子邮件匿名服务所需的安全参数。提供商或其系统管理员需要成为专家在ACN设计也不抽象的隐私度量必要的手动tuning.This项目将利用最新的进展，在混合网络和隐私保护网络数据收集的设计作为我们的积木的基础上，我们可以扩展和增强。由此产生的值得信赖的智能和自适应ACN的持久积极影响将增加采用，从而为英国和全球公众提供强大的隐私。开发的技术、数据集和工具将开源，并将推动英国隐私技术市场的发展。

Anonymous communication networks (ACNs), like Tor and mix networks, protect our sensitive communication meta-data, such as whom we talk with, how often we chat and for how long. This meta-data is privacy sensitive since it can be used to reveal secrets that might otherwise be hidden, even when end-to-end encryption is used. This project is timely since mainstream interest in communication privacy on the Internet has grown since the Snowden-revelations about state-level mass surveillance. However, it is a challenge for ACNs to be deployed since it is hard to tune system parameters that matches the actual realised level of privacy.This is due to the fact that the privacy, security, and performance of ACNsis critically impacted by environmental conditions and user behaviour. For example, it is often assumed thatmessages are not fragmented---broken up into smaller pieces---as they flow over the network. However, the reality is that messages are routinely broken up for performance reasons. Similarly, it is assumed that there is a constant level of user activity, however, users tend to have diurnal activity cycles with bursts and lulls throughout the day.To remedy this current situation, this project aims to bridge the fundamental gaps and provides a framework and a set of methodologies to measure, analyse, and tune ACNs in realistic settings. It does this by pursuing three objectives:1. Mapping & Tuning: New analysis to uncover and formalise relationships between abstract security parameters and real-world network measurements with the view to optimally tune the ACN. 2. Feedback: Investigate novel ACN designs with feedback loops that provide the ability to automatically tune security parameters at run time.3. Use-case validation: Evaluate in targeted use-cases of email, web-browsing, and IoT data collection systems to validate the automated tuning methodology.A common occurrence motivates the need for this project. Let us consider an email provider desiring to provide user anonymity as a market differentiator. Referring to the state-of-the-art in the email-securing mix networks literature it is difficult for the non-expert to reason how to correctly parametrise the mix network for the email provider's particular user base. Mapping & Tuning are the missing ingredients holding back deployment. Given a tuned ACN at start-up time, Feedback can be employed to automatically set and adjust the security parameters necessary for the email anonymity service at run time. The provider nor its system administrator needs to become expert in ACN design nor the abstract privacy metrics necessary for manual tuning.This project will leverage recent advancements in the design of mix networks and privacy-preserving network data collection as the basis of our building blocks from which we can extend and enhance. The lasting positive impact of the resultant trustworthy intelligent and adaptive ACNs will be increased adoption and therefore robust privacy for the UK and global public. The technology, data-sets, and tooling developed will open-sourced and will be a boost to the UK privacy technologies marketplace.