The TNO MPC Lab is a cross-project initiative initiated to improve the overall quality, generality, and reusability in the development of secure Multi-Party Computation (MPC) solutions developed in the numerous (past, ongoing, and future) TNO projects that involve MPC. It consists of generic software components, procedures, and functionalities developed and maintained on a regular basis to facilitate and aid in the development of MPC solutions. The lab strives to boost the development of new protocols and solutions, and decrease time-to-market.
MPC is a subfield of cryptography and an umbrella term consisting of cryptographic techniques that aim to jointly perform computations in a privacy-preserving manner. More precisely, MPC strives to created methods to enable the joint computation of a function over inputs that are distributed among different parties whilst keeping the inputs private. Parties want to ‘learn’ the results of a joint computation without having to share, reveal, or publish, the data that is needed to perform such a computation.
Read our latest articles to see what MPC technology we have developed and how we use MPC in our projects.
Latest in-depth articles
Privacy Enhancing Technologies in Practice
The big data era brought potential for a data-driven society, and led to a new market for PETs. In this article we explore the Dutch-scene of PETs in practice.
Predicting progression of medical status while preserving privacy
New insights into cancer are needed to help improving care and prevention. This requires broad and rich data, for instance to develop machine-learning models that can evaluate treatment outcomes.
Tool: explore privacy-enhancing technologies together
A public support tool for inspiring and facilitating multidisciplinary teams that are interested in applying PETs to their business challenges.
Secure and private statistics with distributed Paillier
We recently used distributed Paillier cryptography to do statistics on sensitive data with unparalleled security and privacy-preservation.
Identifying high-risk factors for diseases while preserving privacy
Multi-Party Computation (MPC) enables using more data from multiple sources to develop accurate models for health care predictions while preserving privacy.
Advanced data linking without breaching privacy
Linking distributed data while safeguarding privacy. An apparent contradiction. MPC technology shows that it can be done.
A targeted, yet privacy-friendly approach for battling poverty
Many citizens entitled to AIO provision are not using it. Multi-Party Computation (MPC) enables to proactive reach out to potential customers in a targeted way.
We believe that opening up the mysteries of advanced cryptography benefits society. Conform the Kerckhoff’s principle the security of the developed MPC solutions does not rely on secrecy but on mathematical principles. The TNO MPC Lab supports Kerckhoff’s principle by publishing open source as a way to validate the theoretical/mathematical correctness of cryptographic protocols as well as their implementations.
The published MPC building blocks and complete solutions can be found on GitHub and PyPI. The mostly used license for TNO MPC lab components is the Apache License, Version 2.0. This allows for easy adoption and flexible usage without enforcing a specific license to (end-)users and contributors of the codebase. We are always open to questions on, suggestions for and contributions to our codebase.
Thomas RooijakkersFunctie:Cyber Security Researcher
Thomas Rooijakkers is a cyber security researcher at TNO. He has a keen interest in fuzzing, and related (automated) vulnerability discovery techniques, as well as privacy-enhancing technologies (PETs), like secure Multi-Party Computation (MPC) and homomorphic encryption (HE). He has a strong drive to bring innovative solutions to industry.
Daniël WormFunctie:Senior consultant
Alex SangersFunctie:Project leader Privacy Enhancing Technologies
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