Use sensitive data
with superior privacy?

Consider our Secure Computation* 

We enable applications
that were previously impossible.

What is Secure Computation?

Privacy 2.0

Secure computation makes GDPR concepts elegantly simple, particularly "data minimization" and "privacy by design". 

How does it work

Privacy is ensured by separation of duties*: sensitive data is encrypted and split among multiple parties, or custodians. The custodians perform computations jointly. Nothing but the pre-agreed output is revealed, as long as the custodians do not collude.

Academic roots

While the first foundational ideas date back to the 80s, the technology became practically relevant and commercially viable only recently.

Why today?  

Demand for privacy increases,
while cost of computation decreases.

Our projects

Anti-money laundering

Develop a solution that: A. efficiently combines similar transactions across different banks and B. then detects money laundering on the combined transactions, with superior privacy and data protection.


Develop a privacy-by-design solution to analyse and manage traffic to office buildings. As part of a privacy-friendly "1.5m-society" solution, our software provides the secure computation layer of a broader building services app.

Cyber Threat Intel Network

Enable a CERT to survey a network of organizations: “Have you seen this threat?” Organizations then anonymously upload their response. CERT shares the aggregated result back to the network.

Core products


Secure Computation engine that directly enables many common functionalities: Benchmarks, Surveys, Operations Research, Pattern Matching, Machine Learning.


De-identification pipeline that prevents tracking personal/confidential data back to its origins.

How to start?

 Proof-of-value in 2-3 months

Design together

First, we define analytical and privacy requirements together. We are available to your stakeholders (business, legal, compliance, technology).

Tailored software

Then, we tailor and license the software to you.
Our source code is available to you for inspection. (We retain intellectual property.)

Support & maintenance

We partner with your IT team or integrator to ensure successful deployment, integration support and run performance.

About us

We are a high-tech software company, determined to transform how organizations handle sensitive data.

Our focus is uncompromised privacy and rapid deployment. We believe robust privacy and business transformation go hand-in-hand.


Niek Bouman


Niek is a senior researcher specialized in privacy-preserving machine learning and secure multi-party computation.

Before, Niek held research positions at TU Eindhoven, ABN AMRO, EPFL (CH), and CWI. 

Niek has a PhD (’12) from Leiden University in quantum cryptography and a MSc from University of Twente in Electrical Engineering.

Toon Segers


Toon is a PhD candidate in applied cryptography at TU Eindhoven, focusing on MPC.

Prior, Toon was Partner at Deloitte, responsible for its Cyber Risk and Blockchain practices in The Netherlands.

Toon worked 10 years at BCG, holds an MBA from Columbia and an MSc from TUE in Applied Math.

Roderick Rodenburg


Roderick is founder of Synple, an automated logistics capacity sharing platform.

Roderick has extensive experience in sensitive data exchange across companies.

Roderick worked 10 years at Unilever, has a MSc from TUE in Mechanical Engineering and an executive education from Harvard Business School.

Case studies of implementations

MPC for blind auctions

First large-scale use of secure computation by Danisco and association of Danish beet growers. Reduction in EU subsidies drove immediate need to trade production rights. (paper describing implementation)

MPC for population studies

Boston Women’s Workforce Council commissioned two studies of wage disparities within organizations in the Great Boston Area (2015-2016). (BWWC explains benefits of using MPC)