31 citations · 142 across the 21 of their papers we have counts for
11 papers · 1 filter
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…
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,…
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…
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…
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…
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…
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…