8 citations · 12 across the 6 of their papers we have counts for
4 papers · 1 filter
FDINet: Protecting against DNN Model Extraction via Feature Distortion Index
Hongwei Yao, Zheng Li, Haiqin Weng +3
Machine Learning as a Service (MLaaS) platforms have gained popularity due to their accessibility, cost-efficiency, scalability, and rapid development capabilities. However, recent…
Practical Privacy Attacks on Vertical Federated Learning
Haiqin Weng, Juntao Zhang, Xingjun Ma +4
Federated learning (FL) is a privacy-preserving learning paradigm that allows multiple parities to jointly train a powerful machine learning model without sharing their private dat…
De-Health: All Your Online Health Information Are Belong to Us
Shouling Ji, Qinchen Gu, Haiqin Weng +4
In this paper, we study the privacy of online health data. We present a novel online health data De-Anonymization (DA) framework, named De-Health. De-Health consists of two phases:…
FDI: Quantifying Feature-based Data Inferability
Shouling Ji, Haiqin Weng, Yiming Wu +3
Motivated by many existing security and privacy applications, e.g., network traffic attribution, linkage attacks, private web search, and feature-based data de-anonymization, in th…