41 citations · 52 across the 6 of their papers we have counts for
6 papers
Attributed Graph Clustering in Collaborative Settings
Rui Zhang, Xiaoyang Hou, Zhihua Tian +5
Graph clustering is an unsupervised machine learning method that partitions the nodes in a graph into different groups. Despite achieving significant progress in exploiting both at…
False Claims against Model Ownership Resolution
Jian Liu, Rui Zhang, Sebastian Szyller +2
Deep neural network (DNN) models are valuable intellectual property of model owners, constituting a competitive advantage. Therefore, it is crucial to develop techniques to protect…
Private Data Valuation and Fair Payment in Data Marketplaces
Zhihua Tian, Jian Liu, Jingyu Li +5
Data valuation is an essential task in a data marketplace. It aims at fairly compensating data owners for their contribution. There is increasing recognition in the machine learnin…
"Adversarial Examples" for Proof-of-Learning
Rui Zhang, Jian Liu, Yuan Ding +3
In S&P '21, Jia et al. proposed a new concept/mechanism named proof-of-learning (PoL), which allows a prover to demonstrate ownership of a machine learning model by proving integri…
FederBoost: Private Federated Learning for GBDT
Zhihua Tian, Rui Zhang, Xiaoyang Hou +4
Federated Learning (FL) has been an emerging trend in machine learning and artificial intelligence. It allows multiple participants to collaboratively train a better global model a…
Learn to Forget: Machine Unlearning via Neuron Masking
Yang Liu, Zhuo Ma, Ximeng Liu +5
Nowadays, machine learning models, especially neural networks, become prevalent in many real-world applications.These models are trained based on a one-way trip from user data: as…