Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference
Secure multi-party computation (MPC) allows users to offload machine learning inference on untrusted servers without having to share their privacy-sensitive data. Despite their strong security properties, MPC-based private inference has not been widely adopted in the real world due to their high communication overhead. When evaluating ReLU layers, MPC protocols incur a significant amount of communication between the parties, making the end-to-end execution time multiple orders slower than its non-private counterpart. This paper presents HummingBird, an MPC framework that reduces the ReLU communication overhead significantly by using only a subset of the bits to evaluate ReLU on a smaller ring. Based on theoretical analyses, HummingBird identifies bits in the secret share that are not crucial for accuracy and excludes them during ReLU evaluation to reduce communication. With its efficient search engine, HummingBird discards 87--91% of the bits during ReLU and still maintains high accuracy. On a real MPC setup involving multiple servers, HummingBird achieves on average 2.03--2.67x end-to-end speedup without introducing any errors, and up to 8.64x average speedup when some amount of accuracy degradation can be tolerated, due to its up to 8.76x communication reduction.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues. Private inference (PI) techniques using cryptographic primitives offer a solution but often have h…
Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference
The large number of ReLU non-linearity operations in existing deep neural networks makes them ill-suited for latency-efficient private inference (PI). Existing techniques to reduce ReLU operations often involve manual ef…
SensitivityDeepReDuce: ReLU Reduction for Fast Private Inference
The recent rise of privacy concerns has led researchers to devise methods for private neural inference -- where inferences are made directly on encrypted data, never seeing inputs. The primary challenge facing private in…
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
We propose AriaNN, a low-interaction privacy-preserving framework for private neural network training and inference on sensitive data. Our semi-honest 2-party computation protocol (with a trusted dealer) leverages functi…
CPUFederated LearningGPUPrivacy Preserving+1Sphynx: ReLU-Efficient Network Design for Private Inference
The emergence of deep learning has been accompanied by privacy concerns surrounding users' data and service providers' models. We focus on private inference (PI), where the goal is to perform inference on a user's data s…