Project description
Protecting sensitive data from malicious tampering
Sending sensitive data to third-party providers for cloud-based machine learning as a service – where companies outsource their AI data processing – exposes users to severe privacy breaches and regulatory penalties. Fully homomorphic encryption solves this privacy issue by processing data while remaining encrypted but lacks a mechanism to verify that the calculated results are actually correct. This vulnerability leaves users highly exposed to hidden errors or malicious data manipulation. The ERC-funded VERIFHE project will develop an open-source library that will introduce verifiable homomorphic encryption technology. Building upon recent cryptographic breakthroughs, researchers will create a proof-of-concept system that combines privacy protection with a verification mechanism. The resulting tool will enable users and industries to confidently adopt secure, trustworthy machine learning services.
Objective
Machine Learning as a Service (MLaaS) is emerging as a cornerstone of the modern digital infrastructure, but its widespread adoption is hampered by critical privacy and integrity concerns. Users must transmit sensitive data to third-party providers, risking privacy breaches and regulatory violations. While Fully Homomorphic Encryption (FHE) offers a powerful solution for privacy by enabling computations on encrypted inputs, it provides no way to verify the correctness of the results. This leaves users vulnerable to errors or malicious manipulation with potentially severe consequences.
The VERIFHE project addresses this critical gap in privacy-preserving MLaaS by providing integrity guarantees through verifiable FHE, a cryptographic primitive that, in addition to enabling computations on encrypted inputs, also provides a mechanism to check their correctness. VERIFHE builds upon a recent breakthrough from the ERC-funded PICOCRYPT project—a novel protocol for verifiable FHE—and aims to transform it into a practical, opensource tool. The project’s primary outcomes will be the first-ever open-source software library for verifiable FHE, and a proof-of-concept MLaaS system that, using the library, offers verifiable and privacy-preserving machine learning inference.
By delivering the first open-source library for verifiable FHE and demonstrating its feasibility in MLaaS, VERIFHE will accelerate adoption of this technology and pave the way for trustworthy machine learning in industrial sectors where privacy and integrity are paramount.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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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.
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.
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.
HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-POC
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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.
28223 Pozuelo De Alarcon
Spain
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.