---
type: Scholarly Article
title: A Security Analysis of CNN Partitioning Strategies for Distributed Inference at the Edge
description: Compares vertical, horizontal, and data partitioning of CNNs across edge devices against an eavesdropper who reconstructs the input image from intercepted intermediate data.
resource: https://doi.org/10.1007/978-3-032-16342-4_15
tags:
  - distributed edge ai
  - cnn security
  - model partitioning
  - input reconstruction
  - privacy
  - distributed edge computing
  - neural networks
  - security
generated:
  by: process:roozbeh.nl/astro-build
  at: 2026-09-06T00:00:00Z
status: stable
sources:
  - id: crossref
    resource: https://api.crossref.org/works/10.1007/978-3-032-16342-4_15
    title: Crossref metadata record
  - id: publisher
    resource: https://doi.org/10.1007/978-3-032-16342-4_15
    title: Publisher record (Springer Nature Switzerland)
  - id: pdf
    resource: https://roozbeh.nl/papers/SPACE_25.pdf
    title: Author manuscript (PDF)
doi: 10.1007/978-3-032-16342-4_15
authors:
  - name: Fatemeh Mehrafrooz
    orcid: 0000-0001-5844-3793
  - name: Roozbeh Siyadatzadeh
    orcid: 0000-0002-2258-4110
  - name: Nele Mentens
    orcid: 0000-0001-8753-7895
  - name: Todor Stefanov
    orcid: 0000-0001-6006-9366
year: 2026
published: 2026-02-12
venue: Security, Privacy, and Applied Cryptography Engineering (SPACE 2025)
venue_type: book-chapter
publisher: Springer Nature Switzerland
volume: "16406"
pages: 262-281
web_page: https://roozbeh.nl/publications/security-analysis-cnn-partitioning-edge/
pdf: https://roozbeh.nl/papers/SPACE_25.pdf
pdf_version: author
bibtex: https://roozbeh.nl/publications/security-analysis-cnn-partitioning-edge.bib
citation_count: 0
citation_count_source: Semantic Scholar Graph API, 2026-09-02
---

# A Security Analysis of CNN Partitioning Strategies for Distributed Inference at the Edge

Fatemeh Mehrafrooz, Roozbeh Siyadatzadeh, Nele Mentens, Todor Stefanov. Security, Privacy, and Applied Cryptography Engineering (SPACE 2025), 2026, pp. 262-281. DOI: [10.1007/978-3-032-16342-4_15](https://doi.org/10.1007/978-3-032-16342-4_15).

# Abstract

The inference of Convolutional Neural Networks (CNNs) at the Edge poses significant challenges due to resource limitations of edge devices. One approach to addressing these challenges is to distribute a CNN model across multiple edge devices. While much attention has been paid to improving the performance, memory utilization, energy efficiency, and robustness of distributed CNN inference at the Edge, security implications of such inference remain largely unexplored. Therefore, in this paper, we investigate the security vulnerabilities of the three main partitioning strategies for distributing CNN models across multiple edge devices, namely vertical partitioning, horizontal partitioning, and data partitioning. More specifically, we assess how accurately an attacker can reconstruct the input image given to a CNN model and predict the image class by eavesdropping on the communication link between two edge devices. We devise a simple, yet realistic attack scenario in which the attacker attempts to reconstruct the input image from intermediate data obtained from the communication link. In order to evaluate the vulnerability of the system, the reconstructed image is fed back into the model to see if its class can be determined. We conduct extensive experiments using different CNN models and datasets. Our results show that data partitioning is less vulnerable to this attack scenario compared to the other partitioning strategies, while vertical partitioning is the most vulnerable.

# Key result

Data partitioning leaked the least reconstructable input information, while vertical partitioning was the most vulnerable across the evaluated models and datasets.

# Cite

F. Mehrafrooz, R. Siyadatzadeh, N. Mentens and T. Stefanov, "A Security Analysis of CNN Partitioning Strategies for Distributed Inference at the Edge," in Security, Privacy, and Applied Cryptography Engineering (SPACE 2025) (Lecture Notes in Computer Science, vol. 16406), Guwahati, India, 2026, pp. 262-281, doi: 10.1007/978-3-032-16342-4_15.

```bibtex
@inproceedings{mehrafrooz2025partitioning,
  author    = {Mehrafrooz, Fatemeh and Siyadatzadeh, Roozbeh and Mentens, Nele and Stefanov, Todor},
  title     = {A Security Analysis of {CNN} Partitioning Strategies for Distributed Inference at the Edge},
  booktitle = {Security, Privacy, and Applied Cryptography Engineering (SPACE 2025)},
  series    = {Lecture Notes in Computer Science},
  volume    = {16406},
  year      = {2026},
  pages     = {262--281},
  publisher = {Springer Nature Switzerland},
  address   = {Guwahati, India},
  doi       = {10.1007/978-3-032-16342-4_15},
  url       = {https://doi.org/10.1007/978-3-032-16342-4_15},
}
```

# Files

* [Web page](https://roozbeh.nl/publications/security-analysis-cnn-partitioning-edge/)
* [Author manuscript (PDF)](https://roozbeh.nl/papers/SPACE_25.pdf)
* [AI-generated audio overview (NotebookLM), 5 min](https://roozbeh.nl/audio/SPACE_25.m4a)
* [AI-generated video overview (NotebookLM), 8 min](https://roozbeh.nl/videos/SPACE_25.mp4)

Author record: [Roozbeh Siyadatzadeh](../person/roozbeh-siyadatzadeh.md).