9 citations · 9 across the 4 of their papers we have counts for
4 papers · 1 filter
Revisiting Continuous Noise Sampling for Multi-Party Differential Privacy
Yucheng Fu, Tianhao Wang
Combining secure multi-party computation (MPC) with differential privacy (DP) enables multiple parties to release aggregate statistics without a trusted curator, and the core primi…
ExpShield: Safeguarding Web Text from Unauthorized Crawling and LLM Exploitation
Ruixuan Liu, Toan Tran, Tianhao Wang +3
As large language models increasingly memorize web-scraped training content, they risk exposing copyrighted or private information. Existing protections require compliance from cra…
Benchmarking Secure Sampling Protocols for Differential Privacy
Yucheng Fu, Tianhao Wang
Differential privacy (DP) is widely employed to provide privacy protection for individuals by limiting information leakage from the aggregated data. Two well-known models of DP are…
PreCurious: How Innocent Pre-Trained Language Models Turn into Privacy Traps
Ruixuan Liu, Tianhao Wang, Yang Cao +1
The pre-training and fine-tuning paradigm has demonstrated its effectiveness and has become the standard approach for tailoring language models to various tasks. Currently, communi…