# Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud

arXiv: https://arxiv.org/abs/1809.03428 · HTML: https://mlsanity.com/paper/1809.03428/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 28 of 28)

- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [Auto-Split: A General Framework of Collaborative Edge-Cloud AI](https://mlsanity.com/paper/2108.13041/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (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)
- [NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/2106.11865/) (2021)
- [Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction](https://mlsanity.com/paper/2206.04864/) (2022)
- [Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation](https://mlsanity.com/paper/2402.17316/) (2024)
- [Packet-Loss-Tolerant Split Inference for Delay-Sensitive Deep Learning in Lossy Wireless Networks](https://mlsanity.com/paper/2104.13629/) (2021)
- [Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks](https://mlsanity.com/paper/2112.09407/) (2021)
- [Task-aware Privacy Preservation for Multi-dimensional Data](https://mlsanity.com/paper/2110.02329/) (2021)
- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [Multi-task Federated Learning with Encoder-Decoder Structure: Enabling Collaborative Learning Across Different Tasks](https://mlsanity.com/paper/2504.09800/) (2025)
- [PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference](https://mlsanity.com/paper/2211.06716/) (2022)
- [Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence](https://mlsanity.com/paper/2101.09225/) (2021)
- [Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning](https://mlsanity.com/paper/2207.11836/) (2022)
- [Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems](https://mlsanity.com/paper/2307.01292/) (2023)
- [Roulette: A Semantic Privacy-Preserving Device-Edge Collaborative Inference Framework for Deep Learning Classification Tasks](https://mlsanity.com/paper/2309.02820/) (2023)
- [Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples](https://mlsanity.com/paper/2310.12707/) (2023)
- [The Fundamental Limits of Least-Privilege Learning](https://mlsanity.com/paper/2402.12235/) (2024)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (2023)
- [LDPKiT: Superimposing Remote Queries for Privacy-Preserving Distillation](https://mlsanity.com/paper/2405.16361/) (2024)
- [A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep Learning](https://mlsanity.com/paper/2105.08876/) (2021)
- [Secure Machine Learning in the Cloud Using One Way Scrambling by Deconvolution](https://mlsanity.com/paper/2111.03125/) (2021)
- [Data Privacy Preservation on the Internet of Things](https://mlsanity.com/paper/2304.00258/) (2023)
- [Coincidental Generation](https://mlsanity.com/paper/2304.01108/) (2023)
- [PriPHiT: Privacy-Preserving Hierarchical Training of Deep Neural Networks](https://mlsanity.com/paper/2408.05092/) (2024)
- [Revealing Unintentional Information Leakage in Low-Dimensional Facial Portrait Representations](https://mlsanity.com/paper/2503.09306/) (2025)
- [Spectrum-Aware Bounds on Invertibility for Privacy-Enhancing Instance Encoding](https://mlsanity.com/paper/2608.23382/) (2026)

## Abstract (arXiv)

The increasing demand for on-device deep learning services calls for a highly efficient manner to deploy deep neural networks (DNNs) on mobile devices with limited capacity. The cloud-based solution is a promising approach to enabling deep learning applications on mobile devices where the large portions of a DNN are offloaded to the cloud. However, revealing data to the cloud leads to potential privacy risk. To benefit from the cloud data center without the privacy risk, we design, evaluate, and implement a cloud-based framework ARDEN which partitions the DNN across mobile devices and cloud data centers. A simple data transformation is performed on the mobile device, while the resource-hungry training and the complex inference rely on the cloud data center. To protect the sensitive information, a lightweight privacy-preserving mechanism consisting of arbitrary data nullification and random noise addition is introduced, which provides strong privacy guarantee. A rigorous privacy budget analysis is given. Nonetheless, the private perturbation to the original data inevitably has a negative impact on the performance of further inference on the cloud side. To mitigate this influence, we propose a noisy training method to enhance the cloud-side network robustness to perturbed data. Through the sophisticated design, ARDEN can not only preserve privacy but also improve the inference performance. To validate the proposed ARDEN, a series of experiments based on three image datasets and a real mobile application are conducted. The experimental results demonstrate the effectiveness of ARDEN. Finally, we implement ARDEN on a demo system to verify its practicality.