Earth Observation products and space telescope observations are space data derived from satellites that can benefit from the application of machine learning to massively exploit the data by introducing other data from public and commercial sources. However, there are concerns that existing technologies used for infrastructure to support the exploitation offer insufficient protection with respect to the risk of data leakage, violation of privacy, unauthorized appropriation or corruption of algorithms. This is deterring users form using the full potential of space data and sharing their own data. European lawmakers are encouraging the adoption of new technologies to allow the exploitation of machine learning without violating ownerships and copyright of the user’s data.
BLENDED, a European Space Agency funded research project led by Space Applications Services is investigating the application of technologies that enable the secure, valuable and efficient collaboration of data and algorithms. The project will be applying the Inter-Planetary File System (IPFS), encryption and blockchain smart contract technologies to prototype a peer-to-peer, decentralised Machine Learning training platform for space data.
Topics that will be addressed include preservation of data/methods ownership, traceability of data use and exploitation, traceability and preservation of copyright, preservation of data privacy, and the ability to manage and process efficiently large volumes of data.
The consortium is:
For more information contact:
Leslie Gale – Marketing & Business Development
