Efficient Arithmetic-and-Comparison Homomorphic Encryption with Space Switching
2026-04-23T07:23:33Z•fbd0d18bfb868fe3362f018440a9bfef4500e80845ac19015d6da49beb891f7d
FHEFVLLM exploit generationNode.jsTLS callbacksVolatility3covert channelsfederated learninggradient inversionhardware assurancehomomorphic encryptionintellectual property leakagemembership inferencememory forensicsmodel inversionprivacyransomware detectionreinforcement learningsoftware supply chainspace switchingstandard cell librariessteganographysupply chaintext steganographyvulnerability discovery
What happened
Collection of new security and privacy research with several high-impact findings: (1) Multiple papers demonstrate practical, data-free Membership Inference / gradient inversion attacks (DECIFR and a data-free MIA) against federated learning applied to hardware assurance, showing that standard-cell-layout priors let adversaries reconstruct SEM/training images and leak IP (technology nodes, layer types) from intercepted model updates. (2) An advance in FV-style fully homomorphic encryption (space switching) enables efficient unified arithmetic and comparison evaluation, improving FHE utility in
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_cr
- Record identifier
- fbd0d18bfb868fe3362f018440a9bfef4500e80845ac19015d6da49beb891f7d
- Enrichment time
- 2026-04-23T07:23:33Z
- AI-assisted enrichment
- Yes
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