Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computing
CoRR(2023)
摘要
In split inference, a deep neural network (DNN) is partitioned to run the
early part of the DNN at the edge and the later part of the DNN in the cloud.
This meets two key requirements for on-device machine learning: input privacy
and computation efficiency. Still, an open question in split inference is
output privacy, given that the outputs of the DNN are observable in the cloud.
While encrypted computing can protect output privacy too, homomorphic
encryption requires substantial computation and communication resources from
both edge and cloud devices. In this paper, we introduce Salted DNNs: a novel
approach that enables clients at the edge, who run the early part of the DNN,
to control the semantic interpretation of the DNN's outputs at inference time.
Our proposed Salted DNNs maintain classification accuracy and computation
efficiency very close to the standard DNN counterparts. Experimental
evaluations conducted on both images and wearable sensor data demonstrate that
Salted DNNs attain classification accuracy very close to standard DNNs,
particularly when the Salted Layer is positioned within the early part to meet
the requirements of split inference. Our approach is general and can be applied
to various types of DNNs. As a benchmark for future studies, we open-source our
code.
更多查看译文
关键词
privacy,split inference
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要