31 citations · 142 across the 21 of their papers we have counts for
21 papers
OFHE: An Electro-Optical Accelerator for Discretized TFHE
Mengxin Zheng, Cheng Chu, Qian Lou +4
This paper presents \textit{OFHE}, an electro-optical accelerator designed to process Discretized TFHE (DTFHE) operations, which encrypt multi-bit messages and support homomorphic…
TrojFair: Trojan Fairness Attacks
Mengxin Zheng, Jiaqi Xue, Yi Sheng +3
Deep learning models have been incorporated into high-stakes sectors, including healthcare diagnosis, loan approvals, and candidate recruitment, among others. Consequently, any bia…
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning
Mengxin Zheng, Jiaqi Xue, Xun Chen +3
Prompt tuning is one of the most effective solutions to adapting a fixed pre-trained language model (PLM) for various downstream tasks, especially with only a few input samples. Ho…
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,…
Primer: Fast Private Transformer Inference on Encrypted Data
Mengxin Zheng, Qian Lou, Lei Jiang
It is increasingly important to enable privacy-preserving inference for cloud services based on Transformers. Post-quantum cryptographic techniques, e.g., fully homomorphic encrypt…
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…
CryptoLight: An Electro-Optical Accelerator for Fully Homomorphic Encryption
Mengxin Zheng, Qian Lou, Fan Chen +2
Fully homomorphic encryption (FHE) protects data privacy in cloud computing by enabling computations to directly occur on ciphertexts. To improve the time-consuming FHE operations,…
Audit and Improve Robustness of Private Neural Networks on Encrypted Data
Jiaqi Xue, Lei Xu, Lin Chen +3
Performing neural network inference on encrypted data without decryption is one popular method to enable privacy-preserving neural networks (PNet) as a service. Compared with regul…
TrojViT: Trojan Insertion in Vision Transformers
Mengxin Zheng, Qian Lou, Lei Jiang
Vision Transformers (ViTs) have demonstrated the state-of-the-art performance in various vision-related tasks. The success of ViTs motivates adversaries to perform backdoor attacks…
MATCHA: A Fast and Energy-Efficient Accelerator for Fully Homomorphic Encryption over the Torus
Lei Jiang, Qian Lou, Nrushad Joshi
Fully Homomorphic Encryption over the Torus (TFHE) allows arbitrary computations to happen directly on ciphertexts using homomorphic logic gates. However, each TFHE gate on state-o…
Automatic Mixed-Precision Quantization Search of BERT
Changsheng Zhao, Ting Hua, Yilin Shen +2
Pre-trained language models such as BERT have shown remarkable effectiveness in various natural language processing tasks. However, these models usually contain millions of paramet…
HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture
Qian Lou, Lei Jiang
Recently Homomorphic Encryption (HE) is used to implement Privacy-Preserving Neural Networks (PPNNs) that perform inferences directly on encrypted data without decryption. Prior PP…
How to Accelerate Capsule Convolutions in Capsule Networks
Zhenhua Chen, Xiwen Li, Qian Lou +1
How to improve the efficiency of routing procedures in CapsNets has been studied a lot. However, the efficiency of capsule convolutions has largely been neglected. Capsule convolut…
CryptoGRU: Low Latency Privacy-Preserving Text Analysis With GRU
Bo Feng, Qian Lou, Lei Jiang +1
Billions of text analysis requests containing private emails, personal text messages, and sensitive online reviews, are processed by recurrent neural networks (RNNs) deployed on pu…
Helix: Algorithm/Architecture Co-design for Accelerating Nanopore Genome Base-calling
Qian Lou, Sarath Janga, Lei Jiang
Nanopore genome sequencing is the key to enabling personalized medicine, global food security, and virus surveillance. The state-of-the-art base-callers adopt deep neural networks…
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference
Qian Lou, Song Bian, Lei Jiang
Hybrid Privacy-Preserving Neural Network (HPPNN) implementing linear layers by Homomorphic Encryption (HE) and nonlinear layers by Garbled Circuit (GC) is one of the most promising…
MindReading: An Ultra-Low-Power Photonic Accelerator for EEG-based Human Intention Recognition
Qian Lou, Wenyang Liu, Weichen Liu +2
A scalp-recording electroencephalography (EEG)-based brain-computer interface (BCI) system can greatly improve the quality of life for people who suffer from motor disabilities. De…
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
Qian Lou, Bo Feng, Geoffrey C. Fox +1
Big data is one of the cornerstones to enabling and training deep neural networks (DNNs). Because of the lack of expertise, to gain benefits from their data, average users have to…
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data
Qian Lou, Lei Jiang
Homomorphic Encryption (HE) is one of the most promising security solutions to emerging Machine Learning as a Service (MLaaS). Leveled-HE (LHE)-enabled Convolutional Neural Network…
AutoQ: Automated Kernel-Wise Neural Network Quantization
Qian Lou, Feng Guo, Lantao Liu +2
Network quantization is one of the most hardware friendly techniques to enable the deployment of convolutional neural networks (CNNs) on low-power mobile devices. Recent network qu…