8 citations · 8 across the 1 of their papers we have counts for
7 papers
GeoIB: Geometry-Aware Information Bottleneck via Statistical-Manifold Compression
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +1
Information Bottleneck (IB) is widely used, but in deep learning, it is usually implemented through tractable surrogates, such as variational bounds or neural mutual information (M…
EVE: Efficient Verification of Data Erasure through Customized Perturbation in Approximate Unlearning
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +2
Verifying whether the machine unlearning process has been properly executed is critical but remains underexplored. Some existing approaches propose unlearning verification methods…
BlindU: Blind Machine Unlearning without Revealing Erasing Data
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +1
Machine unlearning enables data holders to remove the contribution of their specified samples from trained models to protect their privacy. However, it is paradoxical that most unl…
SMS: Self-supervised Model Seeding for Verification of Machine Unlearning
Weiqi Wang, Chenhan Zhang, Zhiyi Tian +1
Many machine unlearning methods have been proposed recently to uphold users' right to be forgotten. However, offering users verification of their data removal post-unlearning is an…
CRFU: Compressive Representation Forgetting Against Privacy Leakage on Machine Unlearning
Weiqi Wang, Chenhan Zhang, Zhiyi Tian +2
Machine unlearning allows data owners to erase the impact of their specified data from trained models. Unfortunately, recent studies have shown that adversaries can recover the era…
SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +1
Deep learning (DL) enabled semantic communications leverage DL to train encoders and decoders (codecs) to extract and recover semantic information. However, most semantic training…