PowerSoftmax: Towards secure LLM Inference Over Encrypted Data

Itamar Zimerman, Allon Adir, Ehud Aharoni, Matan Avitan, Moran Baruch, Nir Drucker, Jenny Lerner, Ramy Masalha, Reut Moshe, Omri Soceanu

Keywords: Secure LLMs, Secure Transformers, Privacy Preserving, Homomorphic Encryption (HE)

TL;DR: We introduce an HE-friendly self-attention variant for large-scale transformers, enabling the first polynomial-based LLMs with a billion parameters and reasoning capabilities.

Abstract:

Modern cryptographic methods for implementing privacy-preserving LLMs such as Homomorphic Encryption require the LLMs to have a polynomial form. Forming such a representation is challenging because Transformers include non-polynomial components, such as Softmax and layer normalization. Previous approaches have either directly approximated pre-trained models with large-degree polynomials, which are less efficient over HE, or replaced non-polynomial components with easier-to-approximate primitives before training, e.g., Softmax with pointwise attention. The latter approach might introduce scalability challenges.

We present a new HE-friendly variant of self-attention that offers a stable form for training and is easy to approximate with polynomials for secure inference. Our work introduces the first polynomial LLMs with 32 layers and over a billion parameters, exceeding the size of previous models by more than tenfold. The resulting models demonstrate reasoning and in-context learning (ICL) capabilities comparable to standard transformers of the same size, representing a breakthrough in the field. Finally, we provide a detailed latency breakdown for each computation over encrypted data, paving the way for further optimization, and explore the differences in inductive bias between transformers relying on our HE-friendly variant and standard transformers. Our code is attached as a supplement.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 4352