Privacy-preserving and distributed processing of public data in hybrid trust networks

One of the increasingly popular paradigms for managing the growing size and complexity of modern ML models is the adoption of collaborative and decentralized approaches. While this has enabled new possibilities in privacy-preserving and scalable frameworks for distributed data analytics and model training over large-scale real-world models, current approaches often assume a uniform trust-levels among participating nodes and emphasise on the privatization of the data locally held by each node. These assumptions overlook realistic scenarios involving varying degrees of trust and differing privacy requirements between nodes. In real-world deployments, it is common for noes in a network to partially use public datasets to perform analytics or train models tailored to their specific needs.