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Encrypted Neural Networks without Overflows

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arXiv:2605. 23096v1 Announce Type: new Abstract: Fully homomorphic encryption (FHE) enables private inference by evaluating neural networks on encrypted data.

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Authors: Philipp Kern, Lorenzo Rovida, Samuel Teuber, Edoardo Manino, Carsten Sinz, Alberto Leporati
Released: 2026-05-25 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Encrypted Neural Networks without Overflows overlaps with clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe". Matching terms: eval, rate, implement, without, full. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2605.23096

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