# Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference

arXiv: https://arxiv.org/abs/1912.09859 · HTML: https://mlsanity.com/paper/1912.09859/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

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

- [Graph Federated Learning for CIoT Devices in Smart Home Applications](https://mlsanity.com/paper/2212.14395/) (2022)
- [PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference](https://mlsanity.com/paper/2211.06716/) (2022)
- [Privacy Failure in Split-LLM Training, The Returned Gradient Nullifies the Decoys](https://mlsanity.com/paper/2609.04382/) (2026)

## Abstract (arXiv)

Executing deep neural networks for inference on the server-class or cloud backend based on data generated at the edge of Internet of Things is desirable due primarily to the limited compute power of edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inference scheme incurs concerns regarding the privacy of the inference data transmitted by the edge devices to the curious backend. This paper presents a lightweight and unobtrusive approach to obfuscate the inference data at the edge devices. It is lightweight in that the edge device only needs to execute a small-scale neural network; it is unobtrusive in that the edge device does not need to indicate whether obfuscation is applied. Extensive evaluation by three case studies of free spoken digit recognition, handwritten digit recognition, and American sign language recognition shows that our approach effectively protects the confidentiality of the raw forms of the inference data while effectively preserving the backend's inference accuracy.