Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption
This work presents a novel protocol for fast secure inference of neural networks applied to computer vision applications. It focuses on improving the overall performance of the online execution by deploying a subset of the model weights in plaintext on the client's machine, in the fashion of SplitNNs. We evaluate our protocol on benchmark neural networks trained on the CIFAR-10 dataset using SEAL via TenSEAL and discuss runtime and security performances. Empirical security evaluation using Membership Inference and Model Extraction attacks showed that the protocol was more resilient under the same attacks than a similar approach also based on SplitNN. When compared to related work, we demonstrate improvements of 2.5x-10x for the inference time and 14x-290x in communication costs.
Code (0)
등록된 구현이 없습니다.
Tasks
Model extractionSimilar Papers 제목 키워드 기반
Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks
Split learning of deep neural networks (SplitNN) has provided a promising solution to learning jointly for the mutual interest of a guest and a host, which may come from different backgrounds, holding features partitione…
Privacy PreservingSecureSplit: Mitigating Backdoor Attacks in Split Learning
Split Learning (SL) offers a framework for collaborative model training that respects data privacy by allowing participants to share the same dataset while maintaining distinct feature sets. However, SL is susceptible to…
VeriSplit: Secure and Practical Offloading of Machine Learning Inferences across IoT Devices
Many Internet-of-Things (IoT) devices rely on cloud computation resources to perform machine learning inferences. This is expensive and may raise privacy concerns for users. Consumers of these devices often have hardware…
Unleashing the Tiger: Inference Attacks on Split Learning
We investigate the security of Split Learning -- a novel collaborative machine learning framework that enables peak performance by requiring minimal resources consumption. In the present paper, we expose vulnerabilities …
Federated LearningOn the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning
Bayesian deep learning and conformal prediction are two methods that have been used to convey uncertainty and increase safety in machine learning systems. We focus on combining Bayesian deep learning with split conformal…
Conformal PredictionDeep Learningimage-classificationImage Classification+2