# Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computing

arXiv: https://arxiv.org/abs/2310.13384 · HTML: https://mlsanity.com/paper/2310.13384/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges](https://mlsanity.com/paper/2103.04505/) (2021)
- [Detailed comparison of communication efficiency of split learning and federated learning](https://mlsanity.com/paper/1909.09145/) (2019)
- [Overlearning Reveals Sensitive Attributes](https://mlsanity.com/paper/1905.11742/) (2019)
- [BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services](https://mlsanity.com/paper/1902.01000/) (2019)
- [Advancements of federated learning towards privacy preservation: from federated learning to split learning](https://mlsanity.com/paper/2011.14818/) (2020)
- [Auto-Split: A General Framework of Collaborative Edge-Cloud AI](https://mlsanity.com/paper/2108.13041/) (2021)
- [Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs](https://mlsanity.com/paper/2105.12049/) (2021)
- [SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems](https://mlsanity.com/paper/2204.04705/) (2022)
- [Unsupervised Information Obfuscation for Split Inference of Neural Networks](https://mlsanity.com/paper/2104.11413/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Novel Hat-Shaped Device-Cloud Collaborative Inference Framework for Large Language Models](https://mlsanity.com/paper/2503.18989/) (2025)
- [ConfusionPrompt: Practical Private Inference for Online Large Language Models](https://mlsanity.com/paper/2401.00870/) (2023)
- [Privacy-Aware Joint DNN Model Deployment and Partitioning Optimization for Collaborative Edge Inference Services](https://mlsanity.com/paper/2502.16091/) (2025)

## Abstract (arXiv)

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.