The project below comes with a full scholarship in the context of the Doctoral Centre for Safe, Ethical and Secure Computing and the York-Maastricht project on Responsible Data Science. Applications will be considered as they arrive until the position is filled.
Secure and Privacy-Preserving FAIR Data Vault Aggregation and Analysis¶
The York-Maastricht project on Responsible Data Science investigates novel mechanisms and technologies for moving, storing, and accessing personal (or otherwise sensitive) information, from centralised databases which are vulnerable to large-scale data leaks and theft, as well as irresponsible data mining, to data vaults stored in end-user devices that comply to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. While this would have been impractical a decade ago, contemporary end-user devices and growing mobile network speeds now make this technically possible. In particular, the project investigates solutions for several challenges related to such a disruptive change in personal data persistence, such as:
- enabling 3rd parties to be granted access to FAIR data vaults in a fully auditable manner
- generating signatures for extracted information in support of provenance in data use
- replicating data in a secure way to minimise the impact of device loss or theft
This PhD project will explore an architecture for querying large numbers of FAIR data vaults to extract aggregate information in a way that ensures privacy and security. This will be achieved by breaking down queries and data into units of work which are meaningless when viewed in isolation, and then delegating the execution of these work units to peers of a computation network comprising end-user devices (fog). Since such devices (and hence the results of their computations) cannot be trusted a priori, the architecture will facilitate Byzantine fault tolerance mechanisms for identifying and isolating untrustworthy nodes of the network as well as mechanisms for rewarding the contribution of computational resources (using techniques similar to those offered by cryptocurrencies).
- Towards model-based development of decentralised peer-to-peer data vaults: A 2020 paper presenting a decentralised architecture for peer-to-peer data vaults we have developed in the context of this project and on which the PhD project can build.