42 citations · 96 across the 16 of their papers we have counts for
16 papers
SurrogatePrompt: Bypassing the Safety Filter of Text-to-Image Models via Substitution
Zhongjie Ba, Jieming Zhong, Jiachen Lei +5
Advanced text-to-image models such as DALLE 2 and Midjourney possess the capacity to generate highly realistic images, raising significant concerns regarding the potential p…
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-…
SWAT: A System-Wide Approach to Tunable Leakage Mitigation in Encrypted Data Stores
Leqian Zheng, Lei Xu, Cong Wang +5
Numerous studies have underscored the significant privacy risks associated with various leakage patterns in encrypted data stores. While many solutions have been proposed to mitiga…
FDINet: Protecting against DNN Model Extraction via Feature Distortion Index
Hongwei Yao, Zheng Li, Haiqin Weng +3
Machine Learning as a Service (MLaaS) platforms have gained popularity due to their accessibility, cost-efficiency, scalability, and rapid development capabilities. However, recent…
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…
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization
Xiaochen Li, Yuke Hu, Weiran Liu +5
Vertical Federated Learning (FL) is a new paradigm that enables users with non-overlapping attributes of the same data samples to jointly train a model without directly sharing the…
L-SRR: Local Differential Privacy for Location-Based Services with Staircase Randomized Response
Han Wang, Hanbin Hong, Li Xiong +2
Location-based services (LBS) have been significantly developed and widely deployed in mobile devices. It is also well-known that LBS applications may result in severe privacy conc…
Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training
Guodong Cao, Zhibo Wang, Xiaowei Dong +4
Adversarial training has been widely explored for mitigating attacks against deep models. However, most existing works are still trapped in the dilemma between higher accuracy and…
Backdoor Defense via Decoupling the Training Process
Kunzhe Huang, Yiming Li, Baoyuan Wu +2
Recent studies have revealed that deep neural networks (DNNs) are vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by poisoning a few trainin…
Feature Importance-aware Transferable Adversarial Attacks
Zhibo Wang, Hengchang Guo, Zhifei Zhang +3
Transferability of adversarial examples is of central importance for attacking an unknown model, which facilitates adversarial attacks in more practical scenarios, e.g., black-box…
e-PoS: Making Proof-of-Stake Decentralized and Fair
Muhammad Saad, Zhan Qin, Kui Ren +2
Blockchain applications that rely on the Proof-of-Work (PoW) have increasingly become energy inefficient with a staggering carbon footprint. In contrast, energy-efficient alternati…
Privacy Enhancement via Dummy Points in the Shuffle Model
Xiaochen Li, Weiran Liu, Hanwen Feng +4
The shuffle model is recently proposed to address the issue of severe utility loss in Local Differential Privacy (LDP) due to distributed data randomization.In the shuffle model, a…
Towards Differentially Private Truth Discovery for Crowd Sensing Systems
Yaliang Li, Houping Xiao, Zhan Qin +5
Nowadays, crowd sensing becomes increasingly more popular due to the ubiquitous usage of mobile devices. However, the quality of such human-generated sensory data varies significan…
Truth Inference on Sparse Crowdsourcing Data with Local Differential Privacy
Haipei Sun, Boxiang Dong, Hui +3
Crowdsourcing has arisen as a new problem-solving paradigm for tasks that are difficult for computers but easy for humans. However, since the answers collected from the recruited p…
Toward Practical Differential Privacy in Smart Grid with Capacity-Limited Rechargeable Batteries
Zijian Zhang, Zhan Qin, Liehuang Zhu +3
The technology of differential privacy, adding a noise drawn from the Laplace distribution, successfully overcomes a difficulty of keeping both the privacy of individual data and t…