activity
20192023
most citedNode-Level Membership Inference Attacks Against Graph Neural Networks

50 citations · 121 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CR20224 cited

Backdoor Attacks in the Supply Chain of Masked Image Modeling

Xinyue Shen, Xinlei He, Zheng Li +3

Masked image modeling (MIM) revolutionizes self-supervised learning (SSL) for image pre-training. In contrast to previous dominating self-supervised methods, i.e., contrastive lear…

cs.SI20222 cited

On Xing Tian and the Perseverance of Anti-China Sentiment Online

Xinyue Shen, Xinlei He, Michael Backes +3

Sinophobia, anti-Chinese sentiment, has existed on the Web for a long time. The outbreak of COVID-19 and the extended quarantine has further amplified it. However, we lack a quanti…

cs.CR202150 cited

Node-Level Membership Inference Attacks Against Graph Neural Networks

Xinlei He, Rui Wen, Yixin Wu +3

Many real-world data comes in the form of graphs, such as social networks and protein structure. To fully utilize the information contained in graph data, a new family of machine l…

cs.LG2021

Quantifying and Mitigating Privacy Risks of Contrastive Learning

Xinlei He, Yang Zhang

Data is the key factor to drive the development of machine learning (ML) during the past decade. However, high-quality data, in particular labeled data, is often hard and expensive…

cs.CR2021

ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models

Yugeng Liu, Rui Wen, Xinlei He +6

Inference attacks against Machine Learning (ML) models allow adversaries to learn sensitive information about training data, model parameters, etc. While researchers have studied,…

cs.CR202038 cited

Stealing Links from Graph Neural Networks

Xinlei He, Jinyuan Jia, Michael Backes +2

Graph data, such as chemical networks and social networks, may be deemed confidential/private because the data owner often spends lots of resources collecting the data or the data…