Yanjiao Chen

Privacy-Preserving Collaborative Model Learning: The Case of Word Vector Training

2018 · IEEE Transactions on Knowledge and Data Engineering

Nowadays, machine learning is becoming a new paradigm for mining hidden knowledge in big data. The collection and manipulation of big data not only create considerable values, but also raise serious privacy concerns. To protect …

SAFE: A General Secure and Fair Auction Framework for Wireless Markets With Privacy Preservation

2020 · IEEE Transactions on Dependable and Secure Computing

With the prosperity of wireless and mobile communications, the allocation of wireless resources, e.g., spectrum channels, femtocell access permissions, and resource blocks of D2D connections, has become a matter of great concern, which leads to …

A Compressive Integrity Auditing Protocol for Secure Cloud Storage

2021 · IEEE/ACM Transactions on Networking

With the widespread application of cloud storage, ensuring the integrity of user outsourced data catches more and more attention. To remotely check the integrity of cloud storage, plenty of protocols have been proposed, implemented by …

Oblivion: Poisoning Federated Learning by Inducing Catastrophic Forgetting

2023

Federated learning is exposed to model poisoning attacks as compromised clients may submit malicious model updates to pollute the global model. To defend against such attacks, robust aggregation rules are designed for the centralized server …

PAPILLON: Efficient and Stealthy Fuzz Testing-Powered Jailbreaks for LLMs

2024 · arXiv (Cornell University)

Large Language Models (LLMs) have excelled in various tasks but are still vulnerable to jailbreaking attacks, where attackers create jailbreak prompts to mislead the model to produce harmful or offensive content. Current jailbreak methods either …

BARBIE: Robust Backdoor Detection Based on Latent Separability

2025

Backdoor attacks are an essential risk to deep learning model sharing.Fundamentally, backdoored models are different from benign models considering latent separability, i.e., distinguishable differences in model latent representations.However, existing methods quantify latent separability by clustering …

Enhancing Membership Inference Attacks in Federated Learning Based on Overfitting Property

2024

Membership inference attacks have been proposed to infer whether a specific sample is in the training dataset of a victim model. Inferred membership may reveal sensitive information, e.g., personal health condition deduced from a disease …