Summary:
Swiss SME offers expertise and a SaaS solution using state-of-the-art deep learning models that allow users to generate synthetic data. The data is then truly anonymous but can still be used for data insights, analysis and internal/external sharing while staying compliant with privacy regulations.
The company is interested in research cooperation agreements (Horizon Europe) and commercial agreements with technical assistance.
Description:
Synthetic data is artificially manufactured data that mimics the original data.
It keeps the statistical distribution as the original data but doesn’t contain sensitive information and can thus be shared internally and externally for further analysis.
Synthetic data is part of Privacy-Enhancing Technologies (PET). Like previous waves of cryptography, PET’s adoption could unlock a trillion dollars opportunity by helping extract more value from existing data, driving the creation of even more of it and enabling a new generation of services and use-cases.
It removes Personally Identifiable Information (PII) and thus is considered being fully anonymized. Synthetic data cannot be reverse engineered as it is the case with pseudonymisation. It allows to be fully compliant with existing regulations and to prevent from potential fines and penalties. It can be scaled to any size and sampled to an unlimited extent, making it highly efficient.
Synthetic data can be applied by anyone handling sensitive data and the fields of application are manifold, such as
- company-wide sharing and artificial intelligence (AI) trainings for company internal sharing
- cross-company sharing and collaborative research for external sharing
Overview of fields of application and synthetic data generator, see pictures below.
The Swiss company's background is in data science and it offers services that use deep learning to generate synthetic data for various file formats. The clients are institutions facing challenges such as compliance laws, fear of data misuse, patient/customer privacy, etc.
The company has identified the following topics in Horizon Europe in which they could provide its expertise and become a valuable partner for a consortium:
TOPIC ID: HORIZON-CL3-2021-CS-01-04
TOPIC ID: HORIZON-CL4-2021-DATA-01-01
TOPIC ID: HORIZON-CL4-2021-DATA-01-03
TOPIC ID: EDF-2021-PROTMOB-D-DMM
TOPIC ID: EDF-2021-OPEN-R-SME
TOPIC ID: EDF-2021-OPEN-RDIS-Open
TOPIC ID: HORIZON-HLTH-2022-TOOL-11-02
TOPIC ID: HORIZON-HLTH-2022-IND-13-02
TOPIC ID: HORIZON-CL6-2022-GOVERNANCE-01-10
TOPIC ID: HORIZON-CL6-2022-GOVERNANCE-01-11
TOPIC ID: HORIZON-INFRA-2022-EOSC-01-03
It is also looking for industrial partners in the health, aerospace, security and defense or manufacturing industry to identify use cases under commercial agreements with technical assistance.
Type (e.g. company, R&D institution…), field of industry and Role of Partner Sought:
Research Cooperation Agreement:
- Coordinators of a Horizon Europe proposal such as:
TOPIC ID: HORIZON-CL3-2021-CS-01-04
TOPIC ID: HORIZON-CL4-2021-DATA-01-01
TOPIC ID: HORIZON-CL4-2021-DATA-01-03
TOPIC ID: EDF-2021-PROTMOB-D-DMM
TOPIC ID: EDF-2021-OPEN-R-SME
TOPIC ID: EDF-2021-OPEN-RDIS-Open
TOPIC ID: HORIZON-HLTH-2022-TOOL-11-02
TOPIC ID: HORIZON-HLTH-2022-IND-13-02
TOPIC ID: HORIZON-CL6-2022-GOVERNANCE-01-10
TOPIC ID: HORIZON-CL6-2022-GOVERNANCE-01-11
TOPIC ID: HORIZON-INFRA-2022-EOSC-01-03
Commercial agreements with technical assistance:
- industry partners from health, aerospace, security and defense or manufacturing
The tasks to be performed by the partner sought:
State and define an internal challenge concerning data sharing such as:
- cannot share data internally/externally because of privacy regulations
- cannot perform data analytics because of lack of data
- partial/no access to data
A use case for implementing synthetic data is then derived from the definition of the challenges.
Stage of Development:
Already on the market
IPR Status:
Copyright
External code:
TOCH20210817001
