Paper pairs ML-KEM and BGV for encrypted health-data processing
Original title: An Agile Homomorphic Encryption and ML-KEM Scheme for Privacy-Preserving Health Data Processing
The authors select parameters and show feasibility with a proof-of-concept aimed at an embedded use case.
A cs.CR paper proposes an agile scheme that combines ML-KEM, the post-quantum key-encapsulation standard, with the BGV fully homomorphic encryption scheme for privacy-preserving health-data processing. The authors say FHE remains complex despite recent efficiency gains, and that several FHE schemes and ML-KEM both rest on Learning With Errors variants. They describe parameter selection and a proof-of-concept implementation intended to show the scheme is feasible for an embedded use case. No deployment, benchmark figures or vulnerability is reported.
- Security of the cited FHE schemes is based on LWE variants.
- The target setting is encrypted computation on health data.
- Feasibility is shown only with a proof-of-concept implementation.
From the original article
1 min read at arxiv.orgThe storage and processing of health data raise serious privacy concerns; therefore, the data must be encrypted. Fully Homomorphic Encryption (FHE) allows computations on encrypted data, which makes it a very promising technology in this context. However, although the efficiency of FHE algorithms has improved in recent years, they remain very complex.
By Daniel Resende · arxiv.org
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