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cs.CR2024
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling
Shaowei Wang, Changyu Dong, Xiangfu Song +4
In data-driven applications, preserving user privacy while enabling valuable computations remains a critical challenge. Technologies like differential privacy have been pivotal in…
cs.CR2023★ 1 cited
BAGEL: Backdoor Attacks against Federated Contrastive Learning
Yao Huang, Kongyang Chen, Jiannong Cao +5
Federated Contrastive Learning (FCL) is an emerging privacy-preserving paradigm in distributed learning for unlabeled data. In FCL, distributed parties collaboratively learn a glob…
cs.CR2023
Differentially Private Numerical Vector Analyses in the Local and Shuffle Model
Shaowei Wang, Jin Li, Yuntong Li +2
Numerical vector aggregation plays a crucial role in privacy-sensitive applications, such as distributed gradient estimation in federated learning and statistical analysis of key-v…