# A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning

arXiv: https://arxiv.org/abs/2405.04115 · HTML: https://mlsanity.com/paper/2405.04115/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2024.

## Built on by (most cited first, 17 of 17)

- [Model Inversion Attacks: A Survey of Approaches and Countermeasures](https://mlsanity.com/paper/2411.10023/) (2024)
- [CapRecover: A Cross-Modality Feature Inversion Attack Framework on Vision Language Models](https://mlsanity.com/paper/2507.22828/) (2025)
- [DRAG: Data Reconstruction Attack using Guided Diffusion](https://mlsanity.com/paper/2509.11724/) (2025)
- [Split Adaptation for Pre-trained Vision Transformers](https://mlsanity.com/paper/2503.00441/) (2025)
- [A Taxonomy of Attacks and Defenses in Split Learning](https://mlsanity.com/paper/2505.05872/) (2025)
- [FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection](https://mlsanity.com/paper/2502.21012/) (2025)
- [Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing](https://mlsanity.com/paper/2507.10494/) (2025)
- [A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraints](https://mlsanity.com/paper/2507.12979/) (2025)
- [Oops!... They Stole it Again: Attacks on Split Learning](https://mlsanity.com/paper/2508.10598/) (2025)
- [Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization](https://mlsanity.com/paper/2602.15304/) (2026)
- [Revisiting the Privacy Risks of Split Inference: A GAN-Based Data Reconstruction Attack via Progressive Feature Optimization](https://mlsanity.com/paper/2508.20613/) (2025)
- [Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directions](https://mlsanity.com/paper/2510.22909/) (2025)
- [Robust Client-Server Watermarking for Split Federated Learning](https://mlsanity.com/paper/2511.13598/) (2025)
- [Enabling Federated Inference via Unsupervised Consensus Embedding](https://mlsanity.com/paper/2605.05718/) (2026)
- [LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections](https://mlsanity.com/paper/2605.13265/) (2026)
- [Keyed Nonlinear Transform: Lightweight Privacy-Enhancing Feature Sharing for Medical Image Analysis](https://mlsanity.com/paper/2605.14123/) (2026)
- [Don't Send What You Don't Need: Question-Guided Token Pruning as a Privacy Defense for Vision-Language Models](https://mlsanity.com/paper/2609.15671/) (2026)

## Abstract (arXiv)

Split Learning (SL) is a distributed learning framework renowned for its privacy-preserving features and minimal computational requirements. Previous research consistently highlights the potential privacy breaches in SL systems by server adversaries reconstructing training data. However, these studies often rely on strong assumptions or compromise system utility to enhance attack performance. This paper introduces a new semi-honest Data Reconstruction Attack on SL, named Feature-Oriented Reconstruction Attack (FORA). In contrast to prior works, FORA relies on limited prior knowledge, specifically that the server utilizes auxiliary samples from the public without knowing any client's private information. This allows FORA to conduct the attack stealthily and achieve robust performance. The key vulnerability exploited by FORA is the revelation of the model representation preference in the smashed data output by victim client. FORA constructs a substitute client through feature-level transfer learning, aiming to closely mimic the victim client's representation preference. Leveraging this substitute client, the server trains the attack model to effectively reconstruct private data. Extensive experiments showcase FORA's superior performance compared to state-of-the-art methods. Furthermore, the paper systematically evaluates the proposed method's applicability across diverse settings and advanced defense strategies.