Network Structures as an Attack Surface: Topology-Based Privacy Leakage in Federated Learning

• URL: http://arxiv.org/abs/2506.19260v1
• Date: Tue, 24 Jun 2025 02:42:08 GMT
• Title: Network Structures as an Attack Surface: Topology-Based Privacy Leakage in Federated Learning
• Authors: Murtaza Rangwala, Richard O. Sinnott, Rajkumar Buyya,
• Abstract summary: We conduct the first comprehensive analysis of topology-based privacy leakage across realistic adversarial knowledge scenarios.<n>We find that adversaries with varying degrees of structural knowledge can infer sensitive data distribution patterns even under strong differential privacy guarantees.
• Score: 22.97190267309304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning systems increasingly rely on diverse network topologies to address scalability and organizational constraints. While existing privacy research focuses on gradient-based attacks, the privacy implications of network topology knowledge remain critically understudied. We conduct the first comprehensive analysis of topology-based privacy leakage across realistic adversarial knowledge scenarios, demonstrating that adversaries with varying degrees of structural knowledge can infer sensitive data distribution patterns even under strong differential privacy guarantees. Through systematic evaluation of 4,720 attack instances, we analyze six distinct adversarial knowledge scenarios: complete topology knowledge and five partial knowledge configurations reflecting real-world deployment constraints. We propose three complementary attack vectors: communication pattern analysis, parameter magnitude profiling, and structural position correlation, achieving success rates of 84.1%, 65.0%, and 47.2% under complete knowledge conditions. Critically, we find that 80% of realistic partial knowledge scenarios maintain attack effectiveness above security thresholds, with certain partial knowledge configurations achieving performance superior to the baseline complete knowledge scenario. To address these vulnerabilities, we propose and empirically validate structural noise injection as a complementary defense mechanism across 808 configurations, demonstrating up to 51.4% additional attack reduction when properly layered with existing privacy techniques. These results establish that network topology represents a fundamental privacy vulnerability in federated learning systems while providing practical pathways for mitigation through topology-aware defense mechanisms.

Related papers

• Deep Learning Models for Robust Facial Liveness Detection [56.08694048252482]
  This study introduces a robust solution through novel deep learning models addressing the deficiencies in contemporary anti-spoofing techniques.<n>By innovatively integrating texture analysis and reflective properties associated with genuine human traits, our models distinguish authentic presence from replicas with remarkable precision.
  arXiv  Detail & Related papers  (2025-08-12T17:19:20Z)
• From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning [7.228253116465784]
  This work uncovers the hidden risks of DFL topologies by proposing a novel Topology Inference Attack.<n>A taxonomy of topology inference attacks is introduced, categorizing them by the attacker's capabilities and knowledge.<n>The results demonstrate that analyzing only the model of each node can accurately infer the DFL topology, highlighting a critical privacy risk in DFL systems.
  arXiv  Detail & Related papers  (2025-01-06T16:27:53Z)
• FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses [50.921333548391345]
  Federated Learning is a privacy preserving decentralized machine learning paradigm.<n>Recent research has revealed that private ground truth data can be recovered through a gradient technique known as Deep Leakage.<n>This paper introduces the FEDLAD Framework (Federated Evaluation of Deep Leakage Attacks and Defenses), a comprehensive benchmark for evaluating Deep Leakage attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-05T11:42:26Z)
• TopoFR: A Closer Look at Topology Alignment on Face Recognition [58.45515807380505]
  We propose TopoFR, a novel FR model that leverages a topological structure alignment strategy called PTSA and a hard sample mining strategy named SDE.<n> PTSA uses persistent homology to align the topological structures of the input and latent spaces, effectively preserving the structure information and improving the generalization performance of FR model.<n> Experimental results on popular face benchmarks demonstrate the superiority of our TopoFR over the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-14T14:58:30Z)
• GI-NAS: Boosting Gradient Inversion Attacks Through Adaptive Neural Architecture Search [52.27057178618773]
  Gradient Inversion Attacks invert the transmitted gradients in Federated Learning (FL) systems to reconstruct the sensitive data of local clients.<n>A majority of gradient inversion methods rely heavily on explicit prior knowledge, which is often unavailable in realistic scenarios.<n>We propose Neural Architecture Search (GI-NAS), which adaptively searches the network and captures the implicit priors behind neural architectures.
  arXiv  Detail & Related papers  (2024-05-31T09:29:43Z)
• Towards Improving Robustness Against Common Corruptions using Mixture of Class Specific Experts [10.27974860479791]
  This paper introduces a novel paradigm known as the Mixture of Class-Specific Expert Architecture. The proposed architecture aims to mitigate vulnerabilities associated with common neural network structures.
  arXiv  Detail & Related papers  (2023-11-16T20:09:47Z)
• Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey [114.17568992164303]
  Adrial attacks and defenses in machine learning and deep neural network have been gaining significant attention. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks.
  arXiv  Detail & Related papers  (2023-03-11T04:19:31Z)
• Digital Privacy Under Attack: Challenges and Enablers [11.061112334099597]
  We systematically categorize attacks targeting three domains: anonymous data, statistical aggregates, and privacy-preserving models.<n>For each category, we analyze attack methodologies, adversary capabilities, and vulnerability mechanisms.<n>Our analysis reveals that while differential privacy offers strong theoretical guarantees, it faces implementation challenges and potential vulnerabilities to emerging attacks.
  arXiv  Detail & Related papers  (2023-02-18T08:34:34Z)
• A Deep Learning Approach for Ontology Enrichment from Unstructured Text [2.932750332087746]
  Existing information vulnerabilities on attacks, controls, and advisories available on the web provide an opportunity to represent and perform security analytics. Ontology enrichment algorithms based on natural language processing and ML models have issues with contextual extraction of concepts in words, phrases, and sentences. Bidirectional LSTMs trained on a large DB dataset and Wikipedia corpus of 2.8 GB along with Universal Sentence is deployed to enrich ISO-based information security.
  arXiv  Detail & Related papers  (2021-12-16T01:32:21Z)
• Where Did You Learn That From? Surprising Effectiveness of Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning [114.9857000195174]
  A major challenge to widespread industrial adoption of deep reinforcement learning is the potential vulnerability to privacy breaches. We propose an adversarial attack framework tailored for testing the vulnerability of deep reinforcement learning algorithms to membership inference attacks.
  arXiv  Detail & Related papers  (2021-09-08T23:44:57Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)
• A neural anisotropic view of underspecification in deep learning [60.119023683371736]
  We show that the way neural networks handle the underspecification of problems is highly dependent on the data representation. Our results highlight that understanding the architectural inductive bias in deep learning is fundamental to address the fairness, robustness, and generalization of these systems.
  arXiv  Detail & Related papers  (2021-04-29T14:31:09Z)
• FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems [49.577302852655144]
  FACESEC is a framework for fine-grained robustness evaluation of face recognition systems. We study five face recognition systems in both closed-set and open-set settings. We find that accurate knowledge of neural architecture is significantly more important than knowledge of the training data in black-box attacks.
  arXiv  Detail & Related papers  (2021-04-08T23:00:25Z)
• OntoEnricher: A Deep Learning Approach for Ontology Enrichment from Unstructured Text [2.707154152696381]
  Existing information on vulnerabilities, controls, and advisories available on the web provides an opportunity to represent knowledge and perform analytics to mitigate some of the concerns. This necessitates dynamic and automated enrichment of information security. Existing ontology enrichment algorithms based on natural processing and ML models have issues with the contextual extraction of concepts in words, phrases and sentences.
  arXiv  Detail & Related papers  (2021-02-08T09:43:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.