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KIProtect: The security layer for data science and artificial intelligence

Project description

New methods to improve sensitive data security

Data science, machine learning and artificial intelligence are fields that are advancing rapidly. However, the data they want to use and analyse are often sensitive, and protecting them is a difficult challenge. The EU-funded KIProtect project aims to address this challenge with an entirely new, innovative approach for data protection and security that is specifically tailored to Big Data and artificial intelligence. The project will provide a novel data pseudonymisation approach based on modern cryptographic and statistical data transformation methods, allowing customers to easily work with and share sensitive data while ensuring compliance and security.

Objective

The importance of data science, machine learning and artificial intelligence is increasing rapidly. Concepts like smart home, smart cities, connected car, Internet of Things (IoT) and industry 4.0 all require large amounts of data that need to be collected, stored and analyzed. To remain relevant and competitive, companies and public organizations alike need to follow this trend. However, the data they want to use and analyze is often sensitive, and protecting it is a difficult challenge.

KIProtect solves this challenge with an entirely new, innovative approach for data security and data protection that is specifically tailored to big data and artificial intelligence: We provide a novel data pseudonymization approach based on modern cryptographic and statistical data transformation methods. This allows our customers to easily work with and share sensitive data while ensuring compliance and security, enabling them to build data-driven business processes on top of secure data streams. Our technology is currently being tested in several proof of concept (PoC) projects and demonstrably works. It is unique in that it can reliably protect high-dimensional data (e.g. images or time series) while retaining most of the data utility. We therefore have a strong USP and are currently pursuing patent protection for our core algorithm as well, which will grant us a very strong position in the large and fast-growing data security market. We plan to use the H2020 funding to prove the applicability of our approach for specific industries and to develop PoC solutions that enable companies to build secure and robust data processing systems for specific use cases. We have realized a first prototype implementation of our methods as an API and are already working with our first pilot customers to validate our business plan. The European data security market has a volume of more than 1 BN € and grows at 15 % per year. We are confident that we can become a technology leader in it.

Fields of science (EuroSciVoc)

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Programme(s)

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Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

SME-1 - SME instrument phase 1

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) H2020-EIC-SMEInst-2018-2020

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Coordinator

7SCIENTISTS GMBH
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 50 000,00
Address
SACHSISCHE STR. 26
10707 BERLIN
Germany

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SME

The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Yes
Region
Berlin Berlin Berlin
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 71 429,00

Participants (1)

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