Securing the Next Billion Consumer Devices on the Edge

愿景：在这次团契中，我的目标是解决采用以用户为中心的隐私增强技术的一个主要挑战：我们能否利用新颖的架构在消费设备上提供私人、可信、个性化和动态可配置的模型，以满足不同的环境和用户需求？重要的是，此类属性必须为数据完整性和模型真实性/可信性提供保证，同时尊重参与培训和改进此类模型的个人的隐私。这一领域的创新和采用需要设备制造商、平台提供商、网络运营商、监管机构和用户之间的合作。该奖学金的目标将使我们远远超越现状、一刀切的解决方案，为个性化、值得信赖和保密的边缘计算提供一个框架，并能够尊重动态策略，特别是在处理消费者物联网(IoT)设备的敏感模型和数据时。在此奖学金中，我的目标是通过设计和评估一个生态系统来应对这些挑战，在该生态系统中，来自消费者物联网设备的分析和与消费者物联网设备的交互可以在信任模型和真实性的情况下发生，同时启用审计和个性化，从而推动当今全有或全无隐私的界限，并支持新的经济模式。这种方法需要设计超越设备当前受信任的内存和处理限制的功能，以及服务提供商、互联网服务提供商、监管机构、设备制造商和最终用户之间的合作对话和生态系统。通过围绕边缘设备的最新架构和安全功能设计我们的框架，在边缘设备商业化之前，我们提供了模型隐私和以用户为中心的物联网生态系统，其中服务提供商可以信任在用户设备上运行的宝贵模型的真实性、可证明性和可信赖性，而用户不必向这些基于云的集中式系统泄露敏感的个人信息。这种方法将能够执行先进和敏感的基于边缘的分析，而不会危及个人隐私。重要的是，我们的目标是将数据真实性和证明机制集成到我们提出的框架中，以支持对模型及其使用的数据的信任。这种保护隐私的技术能够在不共享原始数据的情况下实现新形式的敏感分析，从而提供可能实现某些敏感(或当前非法)数据分析的法律平衡能力。

Vision: In this fellowship, I aim to address a major challenge in the adoption of user-centred privacy-enhancing technologies: Can we leverage novel architectures to provide private, trusted, personalised, and dynamically- configurable models on consumer devices to cater for heterogenous environments and user requirements? Importantly, such properties must provide assurances for the data integrity and model authenticity/trustworthiness, while respecting the privacy of the individuals taking part in training and improving such models. Innovation and adoption in this space require collaborations between device manufacturers, platform providers, network operators, regulators, and the users. The objectives of this fellowship will take us far beyond the status-quo, one-size-fits-all solutions, providing a framework for personalised, trustworthy, and confidential edge computing, with ability to respect dynamic policies, in particular when dealing with sensitive models and data from the consumer Internet of Things (IoT) devices.In this fellowship, I aim to address these challenges by designing and evaluating an ecosystem where analytics from, and interaction with, consumer IoT devices can happen with trust in the model and authenticity, while enabling auditing and personalisation, hence pushing today's boundaries on all-or-nothing privacy and enabling new economic models. This approach requires designing for capabilities beyond the current trusted memory and processing limitations of the devices, and a cooperative dialogue and ecosystem involving service providers, ISPs, regulators, device manufacturers, and the end users. By designing our framework around the latest architectural and security features in edge devices, before they become commercially available, we provision for Model Privacy and a User-Centred IoT ecosystem, where service providers can have trust in the authenticity, attestability, and trustworthiness of the valuable models running on user devices, without the users having to reveal sensitive personal information to these cloud-based centralised systems. This approach will enable advanced and sensitive edge-based analytics to be performed, without jeopardising the individuals' privacy. Importantly, we aim to integrate mechanisms for data authenticity and attestation into our proposed framework, to enable trust in models and the data used by them. Such privacy-preserving technologies have the capacity to enable new form of sensitive analytics, without sharing raw data and thereby providing legal balancing capabilities that might enable certain sensitive (or currently unlawful) data analysis.