4 papers
Rethinking the Security of DP-SGD: A Corrected Analysis of Differentially Private Machine Learning
Wenhao Wang, Shujie Cui, Hui Cui +1
Differentially Private Stochastic Gradient Descent (DP-SGD) is widely used to protect training data in machine learning. Its privacy guarantee is commonly analyzed through a securi…
Wisdom is Knowing What not to Say: Hallucination-Free LLMs Unlearning via Attention Shifting
Chenchen Tan, Youyang Qu, Xinghao Li +4
The increase in computing power and the necessity of AI-assisted decision-making boost the growing application of large language models (LLMs). Along with this, the potential reten…
Guard-GBDT: Efficient Privacy-Preserving Approximated GBDT Training on Vertical Dataset
Anxiao Song, Shujie Cui, Jianli Bai +3
In light of increasing privacy concerns and stringent legal regulations, using secure multiparty computation (MPC) to enable collaborative GBDT model training among multiple data o…
TimeClave: Oblivious In-enclave Time series Processing System
K. Bagher, S. Cui, X. Yuan +2
Cloud platforms are widely adopted by many systems, such as time series processing systems, to store and process massive amounts of sensitive time series data. Unfortunately, sever…