27 citations · 45 across the 12 of their papers we have counts for
6 papers · 1 filter
A Failure-Free and Efficient Discrete Laplace Distribution for Differential Privacy in MPC
Ivan Tjuawinata, Jiabo Wang, Mengmeng Yang +3
In an MPC-protected distributed computation, although the use of MPC assures data privacy during computation, sensitive information may still be inferred by curious MPC participant…
Privacy-Preserving Federated Unlearning with Certified Client Removal
Ziyao Liu, Huanyi Ye, Yu Jiang +4
In recent years, Federated Unlearning (FU) has gained attention for addressing the removal of a client's influence from the global model in Federated Learning (FL) systems, thereby…
Fair and Differentially Private Distributed Frequency Estimation
Mengmeng Yang, Ivan Tjuawinata, Kwok-Yan Lam +2
In order to remain competitive, Internet companies collect and analyse user data for the purpose of improving user experiences. Frequency estimation is a widely used statistical to…
Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computation
Jenn-Bing Ong, Wee-Keong Ng, Ivan Tjuawinata +6
Data privacy is an important issue for organizations and enterprises to securely outsource data storage, sharing, and computation on clouds / fogs. However, data encryption is comp…
Secure Hot Path Crowdsourcing with Local Differential Privacy under Fog Computing Architecture
Mengmeng Yang, Ivan Tjuawinata, Kwok Yan Lam +2
Crowdsourcing plays an essential role in the Internet of Things (IoT) for data collection, where a group of workers is equipped with Internet-connected geolocated devices to collec…
MPC-enabled Privacy-Preserving Neural Network Training against Malicious Attack
Ziyao Liu, Ivan Tjuawinata, Chaoping Xing +1
The application of secure multiparty computation (MPC) in machine learning, especially privacy-preserving neural network training, has attracted tremendous attention from the resea…