161 citations · 247 across the 12 of their papers we have counts for
12 papers
Differentially Private Paired Table-Image Multimodal Synthesis
Kai Chen, Josephine Lamp, Somesh Jha +1
Differentially private (DP) synthesis has been extensively studied for tabular and image data separately, yet many real-world datasets contain images paired with multivariate tabul…
Beyond One-Size-Fits-All: Neural Networks for Differentially Private Tabular Data Synthesis
Kai Chen, Chen Gong, Tianhao Wang
In differentially private (DP) tabular data synthesis, the consensus is that statistical models are better than neural network (NN)-based methods. However, we argue that this concl…
Secure Machine Learning over Relational Data
Qiyao Luo, Yilei Wang, Zhenghang Ren +3
A closer integration of machine learning and relational databases has gained steam in recent years due to the fact that the training data to many ML tasks is the results of a relat…
Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning
Cengguang Zhang, Junxue Zhang, Di Chai +1
Vertical federated learning (VFL) leverages various privacy-preserving algorithms, e.g., homomorphic encryption or secret sharing based SecureBoost, to ensure data privacy. However…
DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks
Yingzhe He, Guozhu Meng, Kai Chen +2
Machine unlearning has great significance in guaranteeing model security and protecting user privacy. Additionally, many legal provisions clearly stipulate that users have the righ…
Confidential Attestation: Efficient in-Enclave Verification of Privacy Policy Compliance
Weijie Liu, Wenhao Wang, Xiaofeng Wang +9
A trusted execution environment (TEE) such as Intel Software Guard Extension (SGX) runs a remote attestation to prove to a data owner the integrity of the initial state of an encla…