175 citations · 531 across the 38 of their papers we have counts for
9 papers · 1 filter
PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols
Fabrizio Cicala, Weicheng Wang, Tianhao Wang +4
Many proximity-based tracing (PCT) protocols have been proposed and deployed to combat the spreading of COVID-19. In this paper, we take a systematic approach to analyze PCT protoc…
TADO: Time-varying Attention with Dual-Optimizer Model
Yuexin Wu, Tianyu Gao, Sihao Wang +1
The review-based recommender systems are commonly utilized to measure users preferences towards different items. In this paper, we focus on addressing three main problems existing…
Analysis and Verification of Relation between Digitizer's Sampling Properties and Energy Resolution of HPGe Detectors
Jinfu Zhu, Tianhao Wang, Tao Xue +4
The CDEX (China Dark matter Experiment) aims at detection of WIMPs (Weakly Interacting Massive Particles) and 0vbb (Neutrinoless double beta decay) of 76Ge. It now uses ~10 kg HPGe…
Improving Robustness to Model Inversion Attacks via Mutual Information Regularization
Tianhao Wang, Yuheng Zhang, Ruoxi Jia
This paper studies defense mechanisms against model inversion (MI) attacks -- a type of privacy attacks aimed at inferring information about the training data distribution given th…
Answering Multi-Dimensional Range Queries under Local Differential Privacy
Jianyu Yang, Tianhao Wang, Ninghui Li +2
In this paper, we tackle the problem of answering multi-dimensional range queries under local differential privacy. There are three key technical challenges: capturing the correlat…
A Principled Approach to Data Valuation for Federated Learning
Tianhao Wang, Johannes Rausch, Ce Zhang +2
Federated learning (FL) is a popular technique to train machine learning (ML) models on decentralized data sources. In order to sustain long-term participation of data owners, it i…
Continuous Release of Data Streams under both Centralized and Local Differential Privacy
Tianhao Wang, Joann Qiongna Chen, Zhikun Zhang +5
In this paper, we study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). One major challenge is that the maximal possible value can be q…
When Machine Unlearning Jeopardizes Privacy
Min Chen, Zhikun Zhang, Tianhao Wang +3
The right to be forgotten states that a data owner has the right to erase their data from an entity storing it. In the context of machine learning (ML), the right to be forgotten r…
Towards Effective Differential Privacy Communication for Users' Data Sharing Decision and Comprehension
Aiping Xiong, Tianhao Wang, Ninghui Li +1
Differential privacy protects an individual's privacy by perturbing data on an aggregated level (DP) or individual level (LDP). We report four online human-subject experiments inve…