3 papers
cs.CR2026
Maverick: Private and Verifiable LLM Inference Made Practical via Matrix-Vector Multiplication Delegation
Ben Merbaum, Mohammad Amin Raeisi, Wenhao Wang +3
Open-source large language models (LLMs) are increasingly competitive with closed-source models while offering transparency and the ability to run inference without exposing user i…
cs.CR2026
Practical Anonymous Two-Party Gradient Boosting Decision Tree
Chenyu Huang, Fan Zhang, Minxin Du +6
Structured data is well handled by gradient-boosted decision trees (GBDT), which are usually trained on vertically partitioned features across mutually distrustful parties. High sp…
cs.CR2026
Privacy-Preserving Screening for Record Linkage
Chenyu Huang, Fan Zhang, Huangxun Chen +4
In an era dominated by big data and machine learning, establishing valuable data collaboration has never been more critical. However, such collaborations must operate under regulat…