6 citations · 9 across the 6 of their papers we have counts for
6 papers
PrivTune: Efficient and Privacy-Preserving Fine-Tuning of Large Language Models via Device-Cloud Collaboration
Yi Liu, Weixiang Han, Chengjun Cai +2
With the rise of large language models, service providers offer language models as a service, enabling users to fine-tune customized models via uploaded private datasets. However,…
Training with Differential Privacy: A Gradient-Preserving Noise Reduction Approach with Provable Security
Haodi Wang, Tangyu Jiang, Yu Guo +3
Deep learning models have been extensively adopted in various regions due to their ability to represent hierarchical features, which highly rely on the training set and procedures.…
Arondight: Red Teaming Large Vision Language Models with Auto-generated Multi-modal Jailbreak Prompts
Yi Liu, Chengjun Cai, Xiaoli Zhang +2
Large Vision Language Models (VLMs) extend and enhance the perceptual abilities of Large Language Models (LLMs). Despite offering new possibilities for LLM applications, these adva…
FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanism
Jiasi Weng, Jian Weng, Hongwei Huang +2
Data holders, such as mobile apps, hospitals and banks, are capable of training machine learning (ML) models and enjoy many intelligence services. To benefit more individuals lacki…
Golden Grain: Building a Secure and Decentralized Model Marketplace for MLaaS
Jiasi Weng, Jian Weng, Chengjun Cai +2
ML-as-a-service (MLaaS) becomes increasingly popular and revolutionizes the lives of people. A natural requirement for MLaaS is, however, to provide highly accurate prediction serv…
Augmenting Encrypted Search: A Decentralized Service Realization with Enforced Execution
Shengshan Hu, Chengjun Cai, Qian Wang +4
Searchable symmetric encryption (SSE) allows the data owner to outsource an encrypted database to a remote server in a private manner while maintaining the ability for selectively…