@inproceedings{81ae36c667da4eda968df761547fd274,
title = "Finding MNEMON: Reviving Memories of Node Embeddings",
abstract = "Previous security research efforts orbiting around graphs have been exclusively focusing on either (de-)anonymizing the graphs or understanding the security and privacy issues of graph neural networks. Little attention has been paid to understand the privacy risks of integrating the output from graph embedding models (e.g., node embeddings) with complex downstream machine learning pipelines. In this paper, we fill this gap and propose a novel model-agnostic graph recovery attack that exploits the implicit graph structural information preserved in the embeddings of graph nodes. We show that an adversary can recover edges with decent accuracy by only gaining access to the node embedding matrix of the original graph without interactions with the node embedding models. We demonstrate the effectiveness and applicability of our graph recovery attack through extensive experiments.",
keywords = "graph embedding, machine learning security and privacy",
author = "Yun Shen and Yufei Han and Zhikun Zhang and Min Chen and Ting Yu and Michael Backes and Yang Zhang and Gianluca Stringhini",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022 ; Conference date: 07-11-2022 Through 11-11-2022",
year = "2022",
month = nov,
day = "7",
doi = "10.1145/3548606.3559358",
language = "English",
series = "Proceedings of the ACM Conference on Computer and Communications Security",
publisher = "Association for Computing Machinery",
pages = "2643--2657",
booktitle = "CCS 2022 - Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security",
address = "United States",
}