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cs.LG2026
Power-Softmax: Towards Secure LLM Inference over Encrypted Data
Itamar Zimerman, Allon Adir, Ehud Aharoni +7
Modern cryptographic methods for implementing privacy-preserving LLMs such as \gls{HE} require the LLMs to have a polynomial form. Forming such a representation is challenging beca…
cs.LG2025
Efficient Decoding Methods for Language Models on Encrypted Data
Matan Avitan, Moran Baruch, Nir Drucker +2
Large language models (LLMs) power modern AI applications, but processing sensitive data on untrusted servers raises privacy concerns. Homomorphic encryption (HE) enables computati…