SOURCE-LINKED INTELLIGENCE

An Open-Source Library for Verifiable Fully Homomorphic Encryption for Trustworthy Machine Learning

CORDIS · observation · Publication date unknown

An Open-Source Library for Verifiable Fully Homomorphic Encryption for Trustworthy Machine Learning 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 buil

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recordType

award

status

SIGNED

region

EU

value

150000

unit

EUR