18 citations · 35 across the 11 of their papers we have counts for
11 papers
Module-Aware Parameter-Efficient Machine Unlearning on Transformers
Wenjie Bao, Jian Lou, Yuke Hu +5
Transformer has become fundamental to a vast series of pre-trained large models that have achieved remarkable success across diverse applications. Machine unlearning, which focuses…
Towards Mitigating Excessive Forgetting in LLM Unlearning via Entanglement-Guidance with Proxy Constraint
Zhihao Liu, Jian Lou, Yuke Hu +6
Large language models (LLMs) are trained on massive datasets that may include private or copyrighted content. Due to growing privacy and ownership concerns, data owners may request…
Differentially Private Zeroth-Order Methods for Scalable Large Language Model Finetuning
Z Liu, J Lou, W Bao +4
Fine-tuning on task-specific datasets is a widely-embraced paradigm of harnessing the powerful capability of pretrained LLMs for various downstream tasks. Due to the popularity of…
Certified Minimax Unlearning with Generalization Rates and Deletion Capacity
Jiaqi Liu, Jian Lou, Zhan Qin +1
We study the problem of -certified machine unlearning for minimax models. Most of the existing works focus on unlearning from standard statistical learning models that have…
Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory
Xiaochen Li, Weiran Liu, Jian Lou +4
Top- frequent items detection is a fundamental task in data stream mining. Many promising solutions are proposed to improve memory efficiency while still maintaining high accura…
ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach
Yuke Hu, Jian Lou, Jiaqi Liu +4
Over the past years, Machine Learning-as-a-Service (MLaaS) has received a surging demand for supporting Machine Learning-driven services to offer revolutionized user experience acr…
PoisonPrompt: Backdoor Attack on Prompt-based Large Language Models
Hongwei Yao, Jian Lou, Zhan Qin
Prompts have significantly improved the performance of pretrained Large Language Models (LLMs) on various downstream tasks recently, making them increasingly indispensable for a di…
FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis
Yiling He, Jian Lou, Zhan Qin +1
Deep learning classifiers achieve state-of-the-art performance in various risk detection applications. They explore rich semantic representations and are supposed to automatically…
RemovalNet: DNN Fingerprint Removal Attacks
Hongwei Yao, Zheng Li, Kunzhe Huang +3
With the performance of deep neural networks (DNNs) remarkably improving, DNNs have been widely used in many areas. Consequently, the DNN model has become a valuable asset, and its…
PromptCARE: Prompt Copyright Protection by Watermark Injection and Verification
Hongwei Yao, Jian Lou, Kui Ren +1
Large language models (LLMs) have witnessed a meteoric rise in popularity among the general public users over the past few months, facilitating diverse downstream tasks with human-…
FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model
Shuo Shao, Wenyuan Yang, Hanlin Gu +4
Federated learning (FL) is a distributed machine learning paradigm allowing multiple clients to collaboratively train a global model without sharing their local data. However, FL e…