2 citations · 3 across the 34 of their papers we have counts for
15 papers · 1 filter
Memory-Efficient Designs for Word-Wise Universal Fully Homomorphic Encryption
Ardhi Wiratama Baskara Yudha, Erwin Eko Wahyudi, Rian Adam Rajagede +2
Fully Homomorphic Encryption (FHE) enables computation on encrypted data, preserving privacy throughout analysis. While its privacy is very strong, FHE is much slower to execute th…
Conjunctive Poisoning in AI Supply-Chain Applications
Nokimul Hasan Arif, Qian Lou, Mengxin Zheng
Large Language and Vision-Language Models are increasingly deployed through inference pipelines that include prompt wrappers (e.g., templates and post-processing scripts) and confi…
MPC-Patch-Bench: Security-Aware LLM Code Patch for Multi-Party Computation
Yukuan Zhang, Mengxin Zheng, Qian Lou
Repository-level benchmarks for evaluating Large Language Model (LLM) code repair on Secure Multi-Party Computation (MPC) software do not yet exist, and directly transplanting gene…
Efficient Arithmetic-and-Comparison Homomorphic Encryption with Space Switching
Erwin Eko Wahyudi, Yan Solihin, Qian Lou
Fully homomorphic encryption (FHE) enables computation on encrypted data without decryption, making it central to privacy-preserving applications. However, no existing scheme effic…
Privacy-Preserving LLMs Routing
Xidong Wu, Yukuan Zhang, Yuqiong Ji +3
Large language model (LLM) routing has emerged as a critical strategy to balance model performance and cost-efficiency by dynamically selecting services from various model provider…
SecureRouter: Encrypted Routing for Efficient Secure Inference
Yukuan Zhang, Mengxin Zheng, Qian Lou
Cryptographically secure neural network inference typically relies on secure computing techniques such as Secure Multi-Party Computation (MPC), enabling cloud servers to process cl…
RobPI: Robust Private Inference against Malicious Client
Jiaqi Xue, Mengxin Zheng, Qian Lou
The increased deployment of machine learning inference in various applications has sparked privacy concerns. In response, private inference (PI) protocols have been created to allo…
RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance
Miao Lin, Feng Yu, Rui Ning +6
Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the pervasive class imbalance in real-world sce…
PRO: Enabling Precise and Robust Text Watermark for Open-Source LLMs
Jiaqi Xue, Yifei Zhao, Mansour Al Ghanim +4
Text watermarking for large language models (LLMs) enables model owners to verify text origin and protect intellectual property. While watermarking methods for closed-source LLMs a…
Securing Transformer-based AI Execution via Unified TEEs and Crypto-protected Accelerators
Jiaqi Xue, Yifei Zhao, Mengxin Zheng +3
Recent advances in Transformer models, e.g., large language models (LLMs), have brought tremendous breakthroughs in various artificial intelligence (AI) tasks, leading to their wid…
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…
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…
TFHE-Coder: Evaluating LLM-agentic Fully Homomorphic Encryption Code Generation
Mayank Kumar, Jiaqi Xue, Mengxin Zheng +1
Fully Homomorphic Encryption over the torus (TFHE) enables computation on encrypted data without decryption, making it a cornerstone of secure and confidential computing. Despite i…
DataSeal: Ensuring the Verifiability of Private Computation on Encrypted Data
Muhammad Husni Santriaji, Jiaqi Xue, Qian Lou +1
Fully Homomorphic Encryption (FHE) allows computations to be performed directly on encrypted data without needing to decrypt it first. This "encryption-in-use" feature is crucial f…
BoostCom: Towards Efficient Universal Fully Homomorphic Encryption by Boosting the Word-wise Comparisons
Ardhi Wiratama Baskara Yudha, Jiaqi Xue, Qian Lou +2
Fully Homomorphic Encryption (FHE) allows for the execution of computations on encrypted data without the need to decrypt it first, offering significant potential for privacy-prese…