Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applications
Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the computational and communication overhead that is incurred when applied at scale. In this paper, we present an overview of our efforts to bridge the gap between this overhead and practicality for privacy-preserving learning systems using multi-party computation (MPC), zero-knowledge proofs (ZKPs), and fully homomorphic encryption (FHE). Through meticulous hardware/software/algorithm co-design, we show progress towards enabling LLM-scale applications in privacy-preserving settings. We demonstrate the efficacy of our solutions in several contexts, including DNN IP ownership, ethical LLM usage enforcement, and transformer inference.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU
We introduce CryptGPU, a system for privacy-preserving machine learning that implements all operations on the GPU (graphics processing unit). Just as GPUs played a pivotal role in the success of modern deep learning, the…
BIG-bench Machine LearningCPUGPUPrivacy Preserving+1Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
Federated Learning is the current state of the art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol. H…
BIG-bench Machine LearningFederated LearningPrivacy PreservingComputeFHE: A Privacy-Preserving General-Purpose Computation Library
Fully Homomorphic Encryption (FHE) enables computations to be performed directly on encrypted data while preserving data confidentiality. However, its practical applications remain limited by high computational costs and…
Agentic Privacy-Preserving Machine Learning
Privacy-preserving machine learning (PPML) is critical to ensure data privacy in AI. Over the past few years, the community has proposed a wide range of provably secure PPML schemes that rely on various cryptography prim…
Motion Semantics Guided Normalizing Flow for Privacy-Preserving Video Anomaly Detection
As embodied perception systems increasingly bridge digital and physical realms in interactive multimedia applications, the need for privacy-preserving approaches to understand human activities in physical environments ha…
Video Anomaly Detection