TL'DR
To enable ad hoc and trustworthy collaborations and transactions between entities that have not established trust a-priori, we need to re-think the design of data and compute. We need to advance Web3 and Privacy-Enhancing Technologies to build more scalable trustworthy and verifiable compute and data fabrics. This will minimize breach of trust, cut cost and reduce business friction.
Motivation
It is not a too bold claim to state that the Industry 4.0 revolution is transforming not only the technological foundations but often the fundamental business models on which existing vertical industries (e.g., Energy, Transportation, Healthcare, Manufacturing) are currently thriving. Increasingly, these traditionally rather closed industries are characterized by fragmented and open ecosystems of actors providing solution ingredients like sensors, software components, services, AI/ML models, data, integration services, etc. Industry 4.0 automation services - bringing together the digital and physical realms – typically rely on large amount of heterogeneous data that are processed by many different AI/ML models to deliver timely situation-aware automation actions. In many cases, both the data as well as the AI/ML models are provided by a diverse set of actors from the vertical ecosystem, introducing the need for so-called collaborative business creation.
In many cases, we observe considerable friction to build such collaborative businesses when human trust capital exceeds the amount of risk. This prevents the sharing of data and models, in turn leading to under-utilized data and models to “grow the pie” for all. This highlights the main challenge of this project: how can we promote partnerships and incentivize ecosystem actors to share data and AI/ML models in a context where trust has not been established a-priori?
The fragmentation and dynamicity of Industry 4.0 ecosystems make traditional approaches, where the required trust could be established via legal means, less applicable. Such trust has been violated in the past without easy detection. In the future, it is preferred for data to be shared and the models to be run in a trustworthy data ecosystem where the trust needed for cooperation and partnership can be reduced or eliminated via technology.
Our research
We create technologies that enable sharing of data and models between parties that have not established a-priori trust. We do this by means of verifiable provenance and verifiable computations on data, creating scalable software stacks for privacy-preserving - yet verifiable - collaboration in trustless environments that minimize the lead-time to transact. We do this for Industry 4.0 eco-systems and private wireless networks.
