31 citations · 60 across the 6 of their papers we have counts for
6 papers
PrivUn: Unveiling Latent Ripple Effects and Shallow Forgetting in Privacy Unlearning
Xiaoyi Chen, Haoyuan Wang, Siyuan Tang +4
Large language models (LLMs) often memorize private information during training, raising serious privacy concerns. While machine unlearning has emerged as a promising solution, its…
The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks
Xiaoyi Chen, Siyuan Tang, Rui Zhu +7
The rapid advancements of large language models (LLMs) have raised public concerns about the privacy leakage of personally identifiable information (PII) within their extensive tra…
Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering
Rui Zhu, Di Tang, Siyuan Tang +4
Most existing methods to detect backdoored machine learning (ML) models take one of the two approaches: trigger inversion (aka. reverse engineer) and weight analysis (aka. model di…
Stealthy Peers: Understanding Security Risks of WebRTC-Based Peer-Assisted Video Streaming
Siyuan Tang, Eihal Alowaisheq, Xianghang Mi +3
As an emerging service for in-browser content delivery, peer-assisted delivery network (PDN) is reported to offload up to 95\% of bandwidth consumption for video streaming, signifi…
Selective Amnesia: On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models
Rui Zhu, Di Tang, Siyuan Tang +2
In this paper, we present a simple yet surprisingly effective technique to induce "selective amnesia" on a backdoored model. Our approach, called SEAM, has been inspired by the pro…
Clues in Tweets: Twitter-Guided Discovery and Analysis of SMS Spam
Siyuan Tang, Xianghang Mi, Ying Li +2
With its critical role in business and service delivery through mobile devices, SMS (Short Message Service) has long been abused for spamming, which is still on the rise today poss…