4 papers
FedSGT: Exact Federated Unlearning via Sequential Group-based Training
Bokang Zhang, Hong Guan, Hong kyu Lee +3
Federated Learning (FL) enables collaborative, privacy-preserving model training, but supporting the "Right to be Forgotten" is especially challenging because data influences the m…
CACTUSDB: Unlock Co-Optimization Opportunities for SQL and AI/ML Inferences
Lixi Zhou, Kanchan Chowdhury, Lulu Xie +5
There is a growing demand for supporting inference queries that combine Structured Query Language (SQL) and Artificial Intelligence / Machine Learning (AI/ML) model inferences in d…
Privacy and Accuracy-Aware AI/ML Model Deduplication
Hong Guan, Lei Yu, Lixi Zhou +5
With the growing adoption of privacy-preserving machine learning algorithms, such as Differentially Private Stochastic Gradient Descent (DP-SGD), training or fine-tuning models on…
Declarative Privacy-Preserving Inference Queries
Hong Guan, Ansh Tiwari, Summer Gautier +8
Detecting inference queries running over personal attributes and protecting such queries from leaking individual information requires tremendous effort from practitioners. To tackl…