Innovating Works
HORIZON-CL3-2021-CS-01-04
HORIZON-CL3-2021-CS-01-04: Scalable privacy-preserving technologies for cross-border federated computation in Europe involving personal data
ExpectedOutcome:Projects are expected to contribute to some of the following expected outcomes:
Sólo fondo perdido 0 €
European
This call is closed Esta línea ya está cerrada por lo que no puedes aplicar.
An upcoming call for this aid is expected, the exact start date of call is not yet clear.
Presentation: Consortium Consortium: Esta ayuda está diseñada para aplicar a ella en formato consorcio.
Minimum number of participants.
This aid finances Proyectos:

ExpectedOutcome:Projects are expected to contribute to some of the following expected outcomes:

Improved scalable and reliable privacy-preserving technologies for federated processing of personal data and their integration in real-world systemsMore user-friendly solutions for privacy-preserving processing of federated personal data registries by researchersImproving privacy-preserving technologies for cyber threat intelligence and data sharing solutionContribution to promotion of GDPR compliant European data spaces for digital services and research (in synergy with topic DATA-01-2021 of Horizon Europe Cluster 4)Strengthened European ecosystem of open source developers and researchers of privacy-preserving solutions The proposal should provide appropriate indicators to measure its progress and specific impact.


Scope:Using big data for digital services and scientific research brings about new opportunities and challenges. For example, machine learning methods process medical and behavioural data for finding causes and explanations for diseases or health risks. However, a large amount of this data is personal data. Leakage or abuse of this kind of dat... see more

ExpectedOutcome:Projects are expected to contribute to some of the following expected outcomes:

Improved scalable and reliable privacy-preserving technologies for federated processing of personal data and their integration in real-world systemsMore user-friendly solutions for privacy-preserving processing of federated personal data registries by researchersImproving privacy-preserving technologies for cyber threat intelligence and data sharing solutionContribution to promotion of GDPR compliant European data spaces for digital services and research (in synergy with topic DATA-01-2021 of Horizon Europe Cluster 4)Strengthened European ecosystem of open source developers and researchers of privacy-preserving solutions The proposal should provide appropriate indicators to measure its progress and specific impact.


Scope:Using big data for digital services and scientific research brings about new opportunities and challenges. For example, machine learning methods process medical and behavioural data for finding causes and explanations for diseases or health risks. However, a large amount of this data is personal data. Leakage or abuse of this kind of data and potential privacy infringement (e.g. attribute disclosure or membership inference) risks are a cybersecurity threat to individuals, society and economy and an impediment for further developing data spaces involving personal data. Vice versa, adequate protection of this data according to the GDPR can also prevent its full utilization for society. Advanced privacy-preserving computation techniques such as homomorphic encryption, secure multiparty computation, and differential privacy are being researched and have proven promising to address these challenges. However, further research is required to ensure their applicability in real-world use case scenarios. For example, fully homomorphic encryption is not practically applicable in many cases and secure multi-party computation often imposes special infrastructural requirements.

Building on research and innovation in the area of privacy-preserving computation, proposals should address scalability and reliability of privacy-preserving technologies in realistic problem areas and take integration with existing infrastructures and traditional security measures (e.g. access control) into account. They should respond to users’ needs, e.g. for research and digital services in access and data management for citizens geared towards their own profiles (incl. dynamic personalised recommendations for improved cybersecurity) or in personalised medicine, taking into account the gender dimension where relevant. They should further address the legacy variation in personal data types and data models across different organisations in the same business sector and/or across different potential application sectors. A proposed solution should include validation or piloting of privacy-preserving computation in realistic federated data infrastructures and more specifically European data spaces involving personal data (e.g. the EU heath data space). It should be guided by the EU’s high standards concerning the right to privacy, protection of personal data, and the protection of fundamental rights in the digital age. It should ensure, by-design, compliance with data regulations and specifically the GDPR. Wherever possible, solutions should be developed as open source software.

Consortia should bring together interdisciplinary expertise and capacity covering the supply and the demand side, i.e. industry, service providers and end-users. Participation of SMEs is strongly encouraged. Legal expertise should also be incorporated to assess and ensure compliance of the technical project results with data regulations and the GDPR.


Cross-cutting Priorities:EOSC and FAIR data


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Temáticas Obligatorias del proyecto: Temática principal: Privacy policies Privacy by design Trust and privacy Privacy Enhancing Technologies (PET) Big data Authentication protocols/frameworks authorization Homomorphic encryption (user-centric) privacy preservation EOSC and FAIR data Privacy concerns behaviours and practices Health data

Consortium characteristics

Scope European : The aid is European, you can apply to this line any company that is part of the European Community.
Tipo y tamaño de organizaciones: The necessary consortium design for the processing of this aid needs:

characteristics of the Proyecto

Requisitos de diseño por participante : Duración:
Requisitos técnicos: ExpectedOutcome:Projects are expected to contribute to some of the following expected outcomes: ExpectedOutcome:Projects are expected to contribute to some of the following expected outcomes:
Do you want examples? Puedes consultar aquí los últimos proyectos conocidos financiados por esta línea, sus tecnologías, sus presupuestos y sus compañías.
Financial Chapters: The chapters of financing expenses for this line are:
Personnel costs.
Expenses related to personnel working directly on the project are based on actual hours spent, based on company costs, and fixed ratios for certain employees, such as the company's owners.
Subcontracting costs.
Payments to external third parties to perform specific tasks that cannot be performed by the project beneficiaries.
Purchase costs.
They include the acquisition of equipment, amortization, material, licenses or other goods and services necessary for the execution of the project
Other cost categories.
Miscellaneous expenses such as financial costs, audit certificates or participation in events not covered by other categories
Indirect costs.
Overhead costs not directly assignable to the project (such as electricity, rent, or office space), calculated as a fixed 25% of eligible direct costs (excluding subcontracting).
Madurez tecnológica: The processing of this aid requires a minimum technological level in the project of TRL 4:. Los componentes que integran determinado proyecto de innovación han sido identificados y se busca establecer si dichos componentes individuales cuentan con las capacidades para actuar de manera integrada, funcionando conjuntamente en un sistema. + info.
TRL esperado:

Characteristics of financing

Intensidad de la ayuda: Sólo fondo perdido + info
Lost Fund:
0% 25% 50% 75% 100%
For the eligible budget, the intensity of the aid in the form of a lost fund may reach as minimum a 100%.
The funding rate for RIA projects is 100 % of the eligible costs for all types of organizations. The funding rate for RIA projects is 100 % of the eligible costs for all types of organizations.
Guarantees:
does not require guarantees
No existen condiciones financieras para el beneficiario.

Additional information about the call

incentive effect: Esta ayuda no tiene efecto incentivador. + info.
Respuesta Organismo: Se calcula que aproximadamente, la respuesta del organismo una vez tramitada la ayuda es de:
Meses de respuesta:
Muy Competitiva:
non -competitive competitive Very competitive
We do not know the total budget of the line
minimis: Esta línea de financiación NO considera una “ayuda de minimis”. You can consult the regulations here.

other advantages

SME seal: Tramitar esta ayuda con éxito permite conseguir el sello de calidad de “sello pyme innovadora”. Que permite ciertas ventajas fiscales.
HORIZON-CL3-2021-CS-01 Scalable privacy-preserving technologies for cross-border federated computation in Europe involving personal data ExpectedOutcome:Projects are expected to contribute to some of the following expected outcomes: Improved scalable and reliable privacy-pres...
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