3 papers
cs.CL2026
How Much Do Large Language Model Cheat on Evaluation? Benchmarking Overestimation under the One-Time-Pad-Based Framework
Zi Liang, Liantong Yu, Shiyu Zhang +2
Overestimation in evaluating large language models (LLMs) has become an increasing concern. Due to the contamination of public benchmarks or imbalanced model training, LLMs may ach…
cs.DB2025
From Randomized Response to Randomized Index: Answering Subset Counting Queries with Local Differential Privacy
Qingqing Ye, Liantong Yu, Kai Huang +3
Local Differential Privacy (LDP) is the predominant privacy model for safeguarding individual data privacy. Existing perturbation mechanisms typically require perturbing the origin…
cs.CR2025
Dual Utilization of Perturbation for Stream Data Publication under Local Differential Privacy
Rong Du, Qingqing Ye, Yaxin Xiao +3
Stream data from real-time distributed systems such as IoT, tele-health, and crowdsourcing has become an important data source. However, the collection and analysis of user-generat…