mlsanityData to 14 Oct 2026

EdgeLeakage: Membership Information Leakage in Distributed Edge Intelligence Systems

arXiv 2404.16851 ↗First posted 8 Mar 2024cs.CRcited by 1

On the map: LLM safety and security → Membership inference attacks

Abstract (arXiv)

In contemporary edge computing systems, decentralized edge nodes aggregate unprocessed data and facilitate data analytics to uphold low transmission latency and real-time data processing capabilities. Recently, these edge nodes have evolved to facilitate the implementation of distributed machine learning models, utilizing their computational resources to enable intelligent decision-making, thereby giving rise to an emerging domain referred to as edge intelligence. However, within the realm of edge intelligence, susceptibility to numerous security and privacy threats against machine learning models becomes evident. This paper addresses the issue of membership inference leakage in distributed edge intelligence systems. Specifically, our focus is on an autonomous scenario wherein edge nodes collaboratively generate a global model. The utilization of membership inference attacks serves to elucidate the potential data leakage in this particular context. Furthermore, we delve into the examination of several defense mechanisms aimed at mitigating the aforementioned data leakage problem. Experimental results affirm that our approach is effective in detecting data leakage within edge intelligence systems, and the implementation of our defense methods proves instrumental in alleviating this security threat. Consequently, our findings contribute to safeguarding data privacy in the context of edge intelligence systems.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Membership inference attacks, one of 12 directions in LLM safety and security. Builds on 11 of its 21 references that are in the map. First posted on arXiv 8 Mar 2024.

Builds on

11 of 21 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Practical Blind Membership Inference Attack via Differential Comparisons5 Jan

Other references · 10

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Deep Learning with Differential Privacycited by 2,728
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  4. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 498
  5. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
  6. 2020Systematic Evaluation of Privacy Risks of Machine Learning Modelscited by 292
  7. 2019MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examplescited by 197
  8. 2018Machine Learning with Membership Privacy using Adversarial Regularizationcited by 186
  9. 2017Knock Knock, Who's There? Membership Inference on Aggregate Location Datacited by 70
  10. 2020Membership Inference Attacks and Defenses in Classification Modelscited by 16

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 14

Similar, not cited

Close in wording, no citation either way

  1. 2024Exposing the Vulnerability of Decentralized Learning to Membership Inference Attacks Through the Lens of Graph Mixing
  2. 2026SoK: Rethinking Membership Inference in Tabular Machine Learning
  3. 2024Privacy-Aware Multi-Device Cooperative Edge Inference with Distributed Resource Bidding
  4. 2021Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
  5. 2025Win-k: Improved Membership Inference Attacks on Small Language Models
  6. 2024A Method to Facilitate Membership Inference Attacks in Deep Learning Models
  7. 2025An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks
  8. 2022Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning