PryMe, a Universal Framework to Measure the Strength of Privacy-enhancing Technologies

隐私是一项普遍价值，也是人权、安全和言论自由的重要内容。然而，在数字时代，隐私日益受到侵蚀，现有的法律和隐私政策方面的保护是不够的，因为它们不能防止隐私侵犯的发生。相比之下，技术层面的隐私保护，即所谓的隐私增强技术，可以防止隐私侵犯，因此是当前许多研究的主题。显示新隐私增强技术的有效性的一种方法，即它们能够在多大程度上保护隐私，是使用隐私指标来衡量技术提供的隐私量。尽管已经提出了许多隐私度量，但有许多研究表明它们在一致性、可重复性和不同应用领域的适用性方面存在缺点。这是一个重要的问题，因为如果使用弱隐私度量过高估计技术提供的隐私量，则可能导致现实世界中的隐私侵犯。拟议的研究通过评估现有隐私指标的质量，确定其优点和缺点，并在此证据的基础上提出新的，更强大的隐私指标来解决这个问题。我们的目标是创建新的隐私指标，以一致地、可重复地、跨应用领域地衡量隐私增强技术的有效性。为了实现这一目标，我们将(i)创建用于系统评估隐私指标的模块化框架PryMe， （ii）应用PryMe框架来评估跨应用领域的隐私指标，以及（iii）提出适用于每个应用领域的强大的新隐私指标。通过提出一个单一的框架来评估许多应用领域的隐私指标，我们允许来自不同领域的隐私指标的研究思想相互补充，这将改变隐私的测量方式。为了进一步实现这一转变，我们将发布PryMe框架的开源代码，以使其他研究人员能够研究不同的应用领域和新的隐私指标。从长远来看，这将与改进隐私增强技术相关，从而改善最终用户的隐私。隐私测量不仅对改进隐私增强技术很重要，而且对分析隐私与数据效用之间或隐私与安全之间的权衡也很重要。因此，更好的隐私度量不仅可以改善最终用户的隐私，还可以在需要权衡隐私与实用性或安全性的情况下改进决策。更好的隐私指标还可以通过显示隐私问题已经在技术层面上得到解决，从而帮助提高用户对车载网络和智能家居等新技术的接受程度。

Privacy is a universal value and an important matter of human rights, security, and freedom of expression. However, in the digital era privacy is increasingly becoming eroded, and existing protections in terms of laws and privacy policies turn out to be insufficient because they do not prevent privacy violations from happening. In contrast, privacy protections on a technical level, so-called privacy-enhancing technologies, can prevent privacy violations and are thus a topic of much current research.One way to show how effective new privacy-enhancing technologies are, i.e. to what extent they are able to protect privacy, is to use privacy metrics to measure the amount of privacy the technologies provide. Even though many privacy metrics have been proposed, there are many studies showing their shortcomings in terms of consistency, reproducibility, and applicability in different application domains. This is an important issue because use of a weak privacy metric can lead to real-world privacy violations if the privacy metric overestimates the amount of privacy provided by a technology.The proposed research addresses this issue by evaluating the quality of existing privacy metrics, identifying their strengths and weaknesses, and building on this evidence to propose new, much stronger privacy metrics. Our aim is to create novel privacy metrics that measure the effectiveness of privacy-enhancing technologies consistently, reproducibly, and across application domains. To achieve this aim, we will (i) create the modular framework PryMe for the systematic evaluation of privacy metrics, (ii) apply the PryMe framework to evaluate privacy metrics across application domains, and (iii) propose strong new privacy metrics that work in each application domain. By proposing a single framework to evaluate privacy metrics in many application domains, we allow research ideas on privacy metrics from different domains to complement each other, which will transform how privacy is measured. To further this transformation, we will release open source code for the PryMe framework to enable other researchers to study different application domains and new privacy metrics. In the long term, this will be relevant to improve privacy-enhancing technologies, and thereby improve privacy for end users.Privacy measurement is important not only to improve privacy-enhancing technologies, but also to analyse trade-offs between privacy and data utility, or between privacy and security. Better privacy metrics therefore not only improve privacy for end users, but also improve the decision-making in situations when privacy needs to be weighed against utility or security. Better privacy metrics can also help improve the user acceptance of new technologies such as vehicular networks and smart homes by showing that privacy issues have been addressed on a technical level.