PRIMaTE: PRIvacy preserving Multi-compartment Trusted Execution

如今，数十亿用户每天都在使用各种各样的在线服务(例如，网络搜索引擎、基于位置的服务、推荐系统)。这些服务成功的关键是他们的结果的个性化，即向每个用户返回那些更接近他们兴趣的结果。例如，给定由两个不同用户发送的网络搜索查询，搜索引擎通常对搜索结果进行不同的排序，以最适合每个用户的偏好。但是，根据底层应用程序，用户配置文件可能包含有关最终用户的敏感信息。在这种情况下，迫切需要设计一种机制，使用户能够安全地访问在线服务，而不必担心他们的数据会从存储和处理数据的云平台泄露出去。拟议的Primate项目解决了在线服务中的隐私保护问题。我们提出了一种系统，它减少并精确地指定信任假设，同时仍然提供与现有技术相比改进的性能。我们的主要贡献将是系统地将这些服务分解到高度硬件安全的隔间中，在这些隔间中，每个服务只能访问执行所分配任务所必需的数据。例如，在由于攻击者利用一个甚至多个分区的代码中的弱点而造成的安全漏洞的情况下，泄露的数据的影响将受到限制，其影响可以精确地量化。因此，攻击者可能只了解配置文件的某些方面，但无法将其链接到用户。Primate通过利用最近的商用处理器(如2016年推出的Skylake代英特尔处理器)支持的新颖可信执行实现了这一目标。英特尔软件保护扩展(SGX)提供的可信执行是一种颠覆性技术，将影响未来代码和数据的保护方式。Primate将利用可信的执行来设计新颖的隐私保护在线服务。尽管目前对可信执行的研究集中在部署整个遗留应用程序，如在单个可信执行环境(TEE)中部署数据库，或者专注于将现有应用程序分成两部分-可信和不可信的解决方案-Primate的目标是更系统和更细粒度的方法。它的目标是开发一种方法，将保护隐私的在线服务划分为多个互动隔间，每个隔间由TEE实现。因此，每个TEE应该处理尽可能少的数据，并且具有定制的、因此最小的可信计算基础。虽然后者使利用灵长类T恤变得困难，但前者限制了暴露的信息，如果攻击者能够成功闯入T恤。

Nowadays, a wide variety of online services (e.g., web search engines, location-based services, recommender systems) are being used by billions of users on a daily basis.Key to the success of these services is the personalisation of their results, that is returning to each user those results that are closer to her interests. For instance, given a web search query sent by two different users, search engines generally rank differently the search results to best fit each user preferences. However, according to the underlying application, user profiles maycontain sensitive information about end users. In this context, it becomes urgent to devise mechanisms that allow users to securely access online services without fearing that their data will be leaked out from the cloud platforms where it is being stored and processed. The proposed PRIMaTE project addresses privacy-preserving in online services. We propose a system that reduces and precisely specifies trust assumptions, while still providing improved performance compared to the state of the art. Our key contribution will be to systematically decompose these services in strongly hardware-secured compartments, where each them has only access to the essentially necessary data to perform the assigned task. In case of security breaches for example due to attackers exploiting a weakness in the code of one or even multiple compartments, the impact of the leaked data will be kept at bounds and their effect can be precisely quantified. Thus, the attacker might only learn certain aspects of a profile butcannot link it to a user. PRIMaTE achieves this goal by utilizing novel trusted execution supportoffered by recent commodity processors such as the 2016 introduced Skylake generation of Intel processors. Trusted execution as offered by Intel Software Guard Extensions (SGX) is a disruptive technology that will impact how code and data is protected in the future. PRIMaTE will utilize trusted execution to devise novel privacy-preserving online services. While current research on trusted execution focused either on deploying whole legacy applications such as a databases in a single Trusted Execution Environment (TEE) or on ad-hoc solutions to split existing applications into two parts - a trusted and untrusted one - PRIMaTE aims for a more systematic and fine-grained approach. It targets to develop a methodology to split privacy-preserving online services into multiple interacting compartments each implemented by a TEE. Thereby, each TEE should handle as little data as possible and have a tailored and therefore minimal trusted computing base. While the latter makes it hard to exploit a PRIMaTE TEE, the former limits the exposed information if an attacker is able to successfully break into a TEE.