# EdgeLeakage: Membership Information Leakage in Distributed Edge Intelligence Systems

arXiv: https://arxiv.org/abs/2404.16851 · HTML: https://mlsanity.com/paper/2404.16851/

## Key facts

- Cited by 1 papers 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.

## Placed in

- LLM safety and security / [Membership inference attacks](https://mlsanity.com/safety/membership-inference-attacks/)

## Builds on (most cited first, 11 of 11)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models](https://mlsanity.com/paper/1806.01246/) (2018)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [Systematic Evaluation of Privacy Risks of Machine Learning Models](https://mlsanity.com/paper/2003.10595/) (2020)
- [MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples](https://mlsanity.com/paper/1909.10594/) (2019)
- [Machine Learning with Membership Privacy using Adversarial Regularization](https://mlsanity.com/paper/1807.05852/) (2018)
- [Practical Blind Membership Inference Attack via Differential Comparisons](https://mlsanity.com/paper/2101.01341/) (2021)
- [Knock Knock, Who's There? Membership Inference on Aggregate Location Data](https://mlsanity.com/paper/1708.06145/) (2017)
- [Membership Inference Attacks and Defenses in Classification Models](https://mlsanity.com/paper/2002.12062/) (2020)

## Built on by (most cited first, 1 of 1)

- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)

## 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.