secure cloud computing
Federated Secure Computing won the Innovation Award by Stifterverband and has secured three years of funding.
read the full article
(reading time 8 mins, in German language)
why secure computing?
“privacy preserving computation” enables cooperation without data sharing
we all need data. the „new oil“ fuels industry 4.0 and machine learning. scientists and politicians, businesses and media need data to make well-informed decisions. free access to information is becoming a human right.
but data also renders us transparent and vulnerable. the free exchange of data is opposed by the right to privacy. data sovereignty and data protection are paramount. data leaks are a public relation nightmare. large data lakes are a prime target for hackers.
there are countless use cases across industries and functions
machine learning – train neural networks on multiple confidential data sets
clinical research and mobile health – analyze populations without sharing patient data
public sector – combine the information of different departments without the need for access to each others’ databases
pandemic control – COVID-19 contract tracing is a very successful example of privacy-preserving computation across millions of smart devices
industry benchmarking and consulting – compare KPIs and compute best practices without leaking business secrets between competitors
autonomous driving and smart homes – service networks without exposing user data
supply chain resilience and deep analytics – learn about interdependencies in trustless networks
did you know?
how does secure computing work?
secure computing or “privacy-preserving computing” comes in several variants.
secure multiparty computation
secure multiparty computation (SMPC) is the gold standard of privacy-preserving computing. several parties form a completely decentral peer-to-peer network. they exchange encrypted messages (“shares”) which do not reveal anything about their private data. only the result of the computation becomes known to the parties.
there are mathematical proofs of correctness and security. some of the protocols can even guarantee security if all but one single party are “corrupt” and try to circumvent the protocol.
homomorphic encryption (HE) works with a central party, e.g. a cloud provider. however, unlike in regular cloud computing, the central party need NOT have the trust of the data owners.
the individual data owners encrypt their data on-premise before uploading it to the cloud. the cloud infrastructure then computes “blindly” on encrypted data. finally, the result is decrypted by the data owners.
the advantage is the highly efficient and fast computing on cloud infrastructure. however, not every computation can be performed on encrypted data, so use case or implementation may be limited.
machine learning is dependent on large datasets for training. with federated learning (FL) these datasets need not be shared. instead, the machine learning model is trained at the sites where the data is stored. only the trained model is shared.
federated learning is a very active area of research. high performance computing is steadily pushing the boundaries of what is possible on infrastructure available today.
are you ready?
what do we offer?
if you want to discuss secure computing on a more strategic level, we would love to have a conversation.
we provide product-independent objective counseling to companies and institutions.
what is your use case?
request a one-to-one
Federated Secure Computing is a modern architecture for secure and privacy-preserving computation.
it is not a crypto framework of its own, but an open technology platform running various protocols (e.g. secure multiparty computation, federated machine learning)
it offloads the difficult and computing intensive cryptography to the on-prem server or cloud.
it rejects complex universality in favour of small, lean, and efficient microservices
its goal is to free up client-side business logic and render development and operation (DevOps) easy and convenient
it is available as a free Open API 3.0 standard
open source solution fdrtd
fdrtd is a simple and lean entry solution.
it is ideal for first movers to try out secure computation without investment.
installation takes minutes, and a proof-of-principle may be realized in a few hours.
the software acts as a middleware between client-side business logic (your job) and server-side cryptography frameworks (cryptographers’ job)
fdrtd is free and open source
all parties retain full control over how, when and by whom their data is used.
all data remains securely on the owner’s server on-prem or in the cloud. no data is ever shared with other parties.
keep people away from data
data remains server-side all the time without the need to download it, ever.
separation of concerns
cryptography and business logic are cleanly separated through an API
military-grade encryption (*) and mathematically proven protocols protect data in transit and in processing
cryptographic protocols are executed on pure peer-to-peer networks without any central database or trusted third party.
(in case of homomorphic encryption, there is “untrusted” central processing on encrypted data.)
free and open source
our solution is open source, and free, even for commercial use, forever.
CI/CD & DevSecOps features
ready for internet of things (IoT)
clients and servers are small enough to fit on smartphones and other IoT and Edge devices
ready for cloud
servers can be moved freely between on-prem and cloud. there are interfaces for popular IaaS services and runtimes. serverless options available.
small, lean microservices replace complex monoliths. they are simple to develop, deploy, and maintain. they are also far more efficient and faster than universal black boxes.
efficient and sustainable
ressource intensive calculations are offloaded to efficient infrastructure. trade efficiency for security as the use case demands.
users are not locked in to any particular runtime, programming language or tech stack. use whatever tools and technologies bet suit your individual use case and IT ecosystem.
solutions using the same protocols are automatically interoperable, even if different parties use different hardware, software or proprietary legacy systems.
plug and play
combine protocols and microservices as needed, plugins are auto detected
contact your consultants
industry and public sector
Dr. Hendrik Ballhausen
0176 – 38 23 92 81
science and universities
Prof. Dr. Christian Hinske
0152 – 54 88 92 85
community and public relations
Dr. Elisabeth Bießlich-Keller
0152 – 54 92 40 76
about this project
we believe in collaboration, not isolation. society, researchers, corporations, and individuals team up to capture the full value of their proprietary data. intelligent connections turn data into meaningful information.
we understand that modern cryptography enables efficient collaboration while still preserving control, custody, trust, agency, consent, and privacy.
we envision transformative use cases for secure computing throughout industries and practises, whenever one must not, want not or can not share their data with partners and competitors.
we are convinced decentralised data storage and peer-to-peer data flow will make for faster, better, more resilient and ultimatively more sustainable and more democratic information infrastructure.
we challenge current barriers to entry and we do not accept that privacy-preserving computation should be difficult to develop, exclusive and expensive to own, or cumbersome to operate.
we empower anyone without prior knowledge and skills to engage in privacy-preserving computing and federated machine learning.
we foster an ecosystem and a community for users and developers to share and learn.
we help you design transformative use cases and support you in building secure applications.
want to get to know us?
request our white paper
resources for developers
meet us at conferences, hackathons or our weekly developer video call.
for user specific questions, send an email to support: