36 citations · 40 across the 48 of their papers we have counts for
Showing 2023Show all
3 papers · 1 filter
cs.LG2023★ 1 cited
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release
Jie Fu, Qingqing Ye, Haibo Hu +4
Machine learning models are known to memorize private data to reduce their training loss, which can be inadvertently exploited by privacy attacks such as model inversion and member…
cs.DB2023★ 36 cited
Trajectory Data Collection with Local Differential Privacy
Yuemin Zhang, Qingqing Ye, Rui Chen +2
Trajectory data collection is a common task with many applications in our daily lives. Analyzing trajectory data enables service providers to enhance their services, which ultimate…
cs.CR2023
Differential Aggregation against General Colluding Attackers
Rong Du, Qingqing Ye, Yue Fu +4
Local Differential Privacy (LDP) is now widely adopted in large-scale systems to collect and analyze sensitive data while preserving users' privacy. However, almost all LDP protoco…