Biblio

We explore how to build a blind certificate authority (CA). Unlike conventional CAs, which learn the exact identity of those registering a public key, a blind CA can simultaneously validate an identity and provide a certificate binding a public key to it, without ever learning the identity. Blind CAs would therefore allow bootstrapping truly anonymous systems in which no party ever learns who participates. In this work we focus on constructing blind CAs that can bind an email address to a public key. To do so, we first introduce secure channel injection (SCI) protocols. These allow one party (in our setting, the blind CA) to insert a private message into another party's encrypted communications. We construct an efficient SCI protocol for communications delivered over TLS, and use it to realize anonymous proofs of account ownership for SMTP servers. Combined with a zero-knowledge certificate signing protocol, we build the first blind CA that allows Alice to obtain a X.509 certificate binding her email address alice@domain.com to a public key of her choosing without ever revealing “alice” to the CA. We show experimentally that our system works with standard email server implementations as well as Gmail.

Emerging Machine Learning (ML) techniques, such as Deep Neural Network, are widely used in today's applications and services. However, with social awareness of privacy and personal data rapidly rising, it becomes a pressing and challenging societal issue to both keep personal data private and benefit from the data analytics power of ML techniques at the same time. In this paper, we argue that to avoid those costs, reduce latency in data processing, and minimise the raw data revealed to service providers, many future AI and ML services could be deployed on users' devices at the Internet edge rather than putting everything on the cloud. Moving ML-based data analytics from cloud to edge devices brings a series of challenges. We make three contributions in this paper. First, besides the widely discussed resource limitation on edge devices, we further identify two other challenges that are not yet recognised in existing literature: lack of suitable models for users, and difficulties in deploying services for users. Second, we present preliminary work of the first systematic solution, i.e. Zoo, to fully support the construction, composing, and deployment of ML models on edge and local devices. Third, in the deployment example, ML service are proved to be easy to compose and deploy with Zoo. Evaluation shows its superior performance compared with state-of-art deep learning platforms and Google ML services.