5 papers
Rethinking and Red-Teaming Protective Perturbation in Personalized Diffusion Models
Yixin Liu, Ruoxi Chen, Xun Chen +1
Personalized diffusion models (PDMs) have become prominent for adapting pre-trained text-to-image models to generate images of specific subjects using minimal training data. Howeve…
SoK: Can Fully Homomorphic Encryption Support General AI Computation? A Functional and Cost Analysis
Jiaqi Xue, Xin Xin, Wei Zhang +11
Artificial intelligence (AI) increasingly powers sensitive applications in domains such as healthcare and finance, relying on both linear operations (e.g., matrix multiplications i…
LineBreaker: Finding Token-Inconsistency Bugs with Large Language Models
Hongbo Chen, Yifan Zhang, Xing Han +7
Token-inconsistency bugs (TIBs) involve the misuse of syntactically valid yet incorrect code tokens, such as misused variables and erroneous function invocations, which can often l…
Memory-adaptive Depth-wise Heterogeneous Federated Learning
Kai Zhang, Yutong Dai, Hongyi Wang +3
Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data. However, the presence of heterogeneous devi…
zkVC: Fast Zero-Knowledge Proof for Private and Verifiable Computing
Yancheng Zhang, Mengxin Zheng, Xun Chen +5
In the context of cloud computing, services are held on cloud servers, where the clients send their data to the server and obtain the results returned by server. However, the compu…