103 citations · 219 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption
Itamar Zimerman, Moran Baruch, Nir Drucker +3
Designing privacy-preserving deep learning models is a major challenge within the deep learning community. Homomorphic Encryption (HE) has emerged as one of the most promising appr…
Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption
Moran Baruch, Nir Drucker, Gilad Ezov +5
Training large-scale CNNs that during inference can be run under Homomorphic Encryption (HE) is challenging due to the need to use only polynomial operations. This limits HE-based…
A Little Is Enough: Circumventing Defenses For Distributed Learning
Moran Baruch, Gilad Baruch, Yoav Goldberg
Distributed learning is central for large-scale training of deep-learning models. However, they are exposed to a security threat in which Byzantine participants can interrupt or co…
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
Felix Kreuk, Assi Barak, Shir Aviv-Reuven +3
In recent years, deep learning has shown performance breakthroughs in many applications, such as image detection, image segmentation, pose estimation, and speech recognition. Howev…