Trustworthy machine learning

Project Title:

Trustworthy machine learning


Alireza Jolfaei, Iqbal Gondal, Joarder Kamruzzaman

Contact person and email address:

Alireza Jolfaei

A brief description of the project:

The highly distributed nature of IoT along with the sheer scale of mobile and ubiquitous computing poses significant challenges to providing timely processing and exchange of large amounts of data and enabling security and privacy when collecting and processing data. Presently the implementation of analytics extraction models largely exists within vast cloud infrastructures. However, the approach of edge computing pushing the network computation towards the data-source, the IoT or mobile device is a promising approach for simultaneously addressing security, privacy and efficacy challenges. There are however additional challenges in modelling and design to effectively implement such privacy-preserving edge computing at scale for many real-world scenarios. This research will focus on exploring approaches to optimise the privacy and utility of analytics extraction at the end-user or the networks edge addressing adequate systems design to offer privacy/security to users, personalised AI-based products with low network delay and large-scale data-collection and exchange.