12 citations · 23 across the 25 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
BadFair: Backdoored Fairness Attacks with Group-conditioned Triggers
Jiaqi Xue, Qian Lou, Mengxin Zheng
Attacking fairness is crucial because compromised models can introduce biased outcomes, undermining trust and amplifying inequalities in sensitive applications like hiring, healthc…
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…
CryptoTrain: Fast Secure Training on Encrypted Dataset
Jiaqi Xue, Yancheng Zhang, Yanshan Wang +3
Secure training, while protecting the confidentiality of both data and model weights, typically incurs significant training overhead. Traditional Fully Homomorphic Encryption (FHE)…
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…
BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models
Jiaqi Xue, Mengxin Zheng, Yebowen Hu +3
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant information from external knowledge bases to provide more accurate, contextually i…
TrojLLM: A Black-box Trojan Prompt Attack on Large Language Models
Jiaqi Xue, Mengxin Zheng, Ting Hua +4
Large Language Models (LLMs) are progressively being utilized as machine learning services and interface tools for various applications. However, the security implications of LLMs,…
vFHE: Verifiable Fully Homomorphic Encryption with Blind Hash
Qian Lou, Muhammad Santriaji, Ardhi Wiratama Baskara Yudha +2
Fully homomorphic encryption (FHE) is a powerful encryption technique that allows for computation to be performed on ciphertext without the need for decryption. FHE will thus enabl…
SSL-Cleanse: Trojan Detection and Mitigation in Self-Supervised Learning
Mengxin Zheng, Jiaqi Xue, Zihao Wang +4
Self-supervised learning (SSL) is a prevalent approach for encoding data representations. Using a pre-trained SSL image encoder and subsequently training a downstream classifier, i…