This perspective considers ways in which the field of microfluidics can increase its impact by
improving existing technologies and enabling new functionalities. We highlight applications …

Data confidentiality is a central concern in modern computer systems and services, as
sensitive data from users and companies are being increasingly delegated to such systems …

Firms are often reluctant to share data because of mistrust, concerns over control, and other
risks. Multi-party computation (MPC) is a new technique to compute meaningful insights …

Countries across the globe have been pushing strict regulations on the protection of
personal or private data collected. The traditional centralized machine learning method …

Blockchain is a revolutionary technology that has reshaped the trust model among mutually
distrustful peers in a distributed network. While blockchain is well-known for its initial usage …

Federated learning (FL) allows a number of parties collectively train models without
revealing private datasets. There is a possibility of extracting personal or confidential data …

Background Modern biomedical research is data-driven and relies heavily on the re-use and
sharing of data. Biomedical data, however, is subject to strict data protection requirements …

As an essential technology underpinning trusted computing, the trusted execution
environment (TEE) allows one to launch computation tasks on both on-and off-premises …

The tremendous boost in the next generation sequencing technologies and in the “omics”
technologies resulted in the generation of hundreds of gigabytes of data per day. Nowadays …

Companies increasingly innovate data-driven business models, enabling them to create
new products and services. Emerging data ecosystems provide these companies access to …