secure cloud computing

innovation award

Federated Secure Computing won the Innovation Award by Stifterverband and has secured three years of funding.

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(reading time 8 mins, in German language)

(c) Illustration Jens Bonnke / Merton Magazin

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awards bytes for life cloud development healthcare partners Stifterverband


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. connecting datasets turns data into meaningful and valuable information.

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. often, we simply cannot centrally pool data, cannot share our data with others.

fortunately, mathematics has an answer to this problem. with modern cryptography, data may remain completely on-premises, while still enabling collaboration. players join decentralised peer-to-peer networks without trusted third parties or central data pools. they never have to reveal the data in their custody. instead, they retain full control over how when and by whom their data is used. privacy and security are guaranteed by military-grade encryption.

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?

  • 97% of companies believe analytics will improve their competitive position

  • 75% of EU companies believe sharing their data would be a worthwhile business model

  • 31% actually do, creating a EU data market of 78 bn Euros at 7% CAGR

  • those that do not yet cite privacy and security as main roadblockers

  • do you have proprietary data that would become more meaningful and valuable if only you could connect it to others’ data?

  • how does secure computing work?

    available technologies

    secure computing or “privacy-preserving computing” comes in several variants.

    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?

  • request a secure computation structured readiness assessment

  • choose technology and tools

  • build skills and capabilities
  • become a first mover

  • what do we offer?

    consulting

    if you want to discuss secure computing on a more strategic level, we would love to have a conversation.

    if you need advise or support with architecture, implementation or operation, always come talk to us.

    we provide product-independent objective counseling to companies and institutions.

    what is your use case?

    request a one-to-one

    architecture

    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

    image credit: Seventyfourimages / Dreamstime

    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)

    start now

    fdrtd is free and open source

    design principles

    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.

    data remains server-side all the time without the need to download it, ever.

    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.)

    our solution is open source, and free, even for commercial use, forever.

    CI/CD & DevSecOps features

    clients and servers are small enough to fit on smartphones and other IoT and Edge devices

    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.

    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.

    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

    photo by Andrea Piacquadio

    our beliefs

    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.

    our mission

    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

    source code

    fdrtd is available as free and open source software under the MIT license.

    the source code, along with technical documentation and a growing library of protocols and microservices is available at GitHub.

    community

    meet us at conferences, hackathons or our weekly developer video call.

    support

    for user specific questions, send an email to support:

    photo by Olia Danilevich