In this talk, I present our ongoing work on utilising edge-computing to improve the scalability and privacy of user-centred analytics in the context of personal/IoT data. I present an architecture where devices and resources centred around the user, collectively referred to as the edge, can complement the cloud for providing privacy-aware, yet accurate and efficient analytics. I then present the evaluations of the proposed framework for applying privacy-preserving deep learning techniques on a number of exemplar applications, and discuss the broader implications of such approaches for future systems such as the Brave Browser, the Databox platform, and health monitoring applications.
Hamed Haddadi a Senior Lecturer (~Associate Professor) and the Deputy Director of Research in the Dyson School of Design Engineering at Imperial College London. He leads the Systems and Algorithms Laboratory and is an Academic Fellow of the Data Science. He is also a Visiting Professor at Brave Software. https://haddadi.github.io
