# A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics

arXiv: https://arxiv.org/abs/1703.02952 · HTML: https://mlsanity.com/paper/1703.02952/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2017.

## Built on by (most cited first, 30 of 30)

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- [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)
- [Towards Battery-Free Machine Learning and Inference in Underwater Environments](https://mlsanity.com/paper/2202.08174/) (2022)
- [GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning](https://mlsanity.com/paper/2110.03967/) (2021)
- [AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning](https://mlsanity.com/paper/2112.01637/) (2021)
- [Edge-Based Video Analytics: A Survey](https://mlsanity.com/paper/2303.14329/) (2023)
- [Decouple-and-Sample: Protecting sensitive information in task agnostic data release](https://mlsanity.com/paper/2203.13204/) (2022)
- [Information Removal at the bottleneck in Deep Neural Networks](https://mlsanity.com/paper/2210.00891/) (2022)
- [SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention](https://mlsanity.com/paper/2301.12885/) (2023)
- [Hybrid-RACA: Hybrid Retrieval-Augmented Composition Assistance for Real-time Text Prediction](https://mlsanity.com/paper/2308.04215/) (2023)
- [Disentangling private classes through regularization](https://mlsanity.com/paper/2207.02000/) (2022)
- [PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference](https://mlsanity.com/paper/2211.06716/) (2022)
- [Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review](https://mlsanity.com/paper/2306.00687/) (2023)
- [DRAG: Data Reconstruction Attack using Guided Diffusion](https://mlsanity.com/paper/2509.11724/) (2025)
- [Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence](https://mlsanity.com/paper/2101.09225/) (2021)
- [Data Selection for Efficient Model Update in Federated Learning](https://mlsanity.com/paper/2111.03512/) (2021)
- [Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems](https://mlsanity.com/paper/2307.01292/) (2023)
- [Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples](https://mlsanity.com/paper/2310.12707/) (2023)
- [Privacy Safe Representation Learning via Frequency Filtering Encoder](https://mlsanity.com/paper/2208.02482/) (2022)
- [Adjustable Privacy using Autoencoder-based Learning Structure](https://mlsanity.com/paper/2304.03538/) (2023)
- [Secure Machine Learning in the Cloud Using One Way Scrambling by Deconvolution](https://mlsanity.com/paper/2111.03125/) (2021)
- [Privacy-Preserving Speech Representation Learning using Vector Quantization](https://mlsanity.com/paper/2203.09518/) (2022)
- [Learning to Censor by Noisy Sampling](https://mlsanity.com/paper/2203.12192/) (2022)
- [Distributed data analytics](https://mlsanity.com/paper/2203.14088/) (2022)
- [InFi: End-to-End Learning to Filter Input for Resource-Efficiency in Mobile-Centric Inference](https://mlsanity.com/paper/2209.13873/) (2022)
- [Data Privacy Preservation on the Internet of Things](https://mlsanity.com/paper/2304.00258/) (2023)
- [Revisiting the Privacy Risks of Split Inference: A GAN-Based Data Reconstruction Attack via Progressive Feature Optimization](https://mlsanity.com/paper/2508.20613/) (2025)

## Abstract (arXiv)

Internet of Things (IoT) devices and applications are being deployed in our homes and workplaces. These devices often rely on continuous data collection to feed machine learning models. However, this approach introduces several privacy and efficiency challenges, as the service operator can perform unwanted inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger, and more complicated models. In this paper, we present a hybrid approach for breaking down large, complex deep neural networks for cooperative, privacy-preserving analytics. To this end, instead of performing the whole operation on the cloud, we let an IoT device to run the initial layers of the neural network, and then send the output to the cloud to feed the remaining layers and produce the final result. In order to ensure that the user's device contains no extra information except what is necessary for the main task and preventing any secondary inference on the data, we introduce Siamese fine-tuning. We evaluate the privacy benefits of this approach based on the information exposed to the cloud service. We also assess the local inference cost of different layers on a modern handset. Our evaluations show that by using Siamese fine-tuning and at a small processing cost, we can greatly reduce the level of unnecessary, potentially sensitive information in the personal data, and thus achieving the desired trade-off between utility, privacy, and performance.