DATA TRUSTS: IDENTITY AND DATA IN THE DDE
DECADE EXPLORES VALUE CREATION THROUGH DATA FLUIDITY – DATA THAT MOVES DYNAMICALLY WITH GRANULAR ACCESS CONTROL, GENERATING VALUE FOR ITS OWNERS.
We leverage new models for data provenance, commodifying data and the products derived from it, exploring the associated issues of self-sovereign and decentralised identities. The solution is not to put up walls and locally silo or isolate data, but to enhance data fluidity. Society has already begun to push back on organisations that silo and extract value from the deluge of our data. However, the solution is not to put up walls and locally silo or isolate data, but to enhance data fluidity, so it moves dynamically with granular access control to generate value for its owners.
DECENTRALISED DATA AND IDENTITY 02 VALUE FOR ITS OWNERS. GIVING USERS GREATER CONTROL OVER THEIR PERSONAL DATA. DECaDE will empower users to identify areas of value in their personal data and explore novel models for releasing that value through decentralised technologies, giving users greater agency over its use through novel economic and governance models. For example, the concept of Data Trusts is emerging, giving third parties permission to steward or commoditise users’ data for them. This concept raises many questions. What would a decentralised data trust look like, and what value could be extracted from data, in new ways? For example, the recent EPSRC CoMEHeRE project explored commodification of fitness band data to medical insurers in return for micropayments. DECaDE will explore these and novel value models such as how the provenance of data used to train an AI model might be used to define shared ownership (digital equity) of that model when commoditised. Such models may encourage more equitable use of data to create value for individuals, and so mitigate the push back on reluctance for data sharing tensioned against the drive for improved innovation in data hungry AI. DECaDE will also explore the training of AI models on individuals’ data in a privacy preserving manner through decentralised and federated machine learning, a kind of distributed computing, while drawing upon our partner expertise to understand the implications of GDPR.