CERN Living Lab
CERN Living Lab
CERN Living Lab
The project goal is to develop a big-data analytics platform for large-scale studies of data under special constraints, such as information that is privacy-sensitive, or that has a varying level of quality, associated provenance information, or signal-to-noise ratio. Ethical considerations are also considered when necessary. This will serve as a proof-of-concept for federating and analysing heterogeneous data from diverse sources, in particular for medical and biological research, using ideas and expertise coming from CERN and the broader high-energy physics community.
CERN is a living laboratory, with around 15,000 people coming to work at its main campuses every day. For operational purposes, CERN collects data related to health, safety, the environment, and other aspects of daily life at the lab. Creating a platform to collate and enable intelligent management and use of this data — while respecting privacy and other ethical and legal obligations — offers the potential to improve life at the lab. At the same time, such a platform provides an ideal testbed for exploring new data analytics technologies, algorithms and tools, including ML/DL methods, encryption schemes, or block-chain-based ledgers. It also provides a natural bridge to collaborate with other scientific research domains, such as medical research and biology.
This project is being carried out in the context of CERN's strategy for knowledge transfer to medical applications, led by CERN's Knowledge Transfer group.
The CERN Living Lab project started formally in June 2019. A kick-off meeting was held with all project partners to discuss in detail shared interests and objectives. A second meeting took place in December 2019, focusing on the architecture and requirements for the big-data analytics platform. The platform architecture was defined and agreed. Also, four specific sub-projects were defined to address the data life-cycle in the presence of sensitive information, the data ingestion from foreign sources, the possibility of dynamically detecting and addressing the level of privacy protection required by different data transfer requests, and the use of homomorphic encryption as a possible privacy-preserving approach for cloud-based data analysis
In 2020, a proof-of-concept platform will be established, and a number of selected use cases will be deployed. Specifically, investigations will include classification and detection of the symptoms of Parkinson’s disease from wearable devices, optimisation of homomorphic encryption techniques for deep learning, and medical image analysis.
- T. Aliyev, Meaningful Control of AI and Machine Ethics (7 June). Presented at Big Data in Medicine: Challenges and Opportunities, CERN, Geneva, 2019. cern.ch/go/J7CF
- A. Di Meglio, The CERN Living Lab Initiative (20 June). Presented at CERN Information Technology for the Hospitals, HUG, Geneva, 2019. cern.ch/go/Fld8
- T. Aliyev, Interpretability and Accountability as Necessary Pieces for Machine Ethics (2 July). Presented at Implementing Machine Ethics Workshop, UCD, Dublin, 2019. cern.ch/go/7c6d