31 citations · 151 across the 19 of their papers we have counts for
19 papers
QDoor: Exploiting Approximate Synthesis for Backdoor Attacks in Quantum Neural Networks
Cheng Chu, Fan Chen, Philip Richerme +1
Quantum neural networks (QNNs) succeed in object recognition, natural language processing, and financial analysis. To maximize the accuracy of a QNN on a Noisy Intermediate Scale Q…
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…
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…
QTrojan: A Circuit Backdoor Against Quantum Neural Networks
Cheng Chu, Lei Jiang, Martin Swany +1
We propose a circuit-level backdoor attack, \textit{QTrojan}, against Quantum Neural Networks (QNNs) in this paper. QTrojan is implemented by few quantum gates inserted into the va…
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…
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…
EXMA: A Genomics Accelerator for Exact-Matching
Lei Jiang, Farzaneh Zokaee
Genomics is the foundation of precision medicine, global food security and virus surveillance. Exact-match is one of the most essential operations widely used in almost every step…
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…
FindeR: Accelerating FM-Index-based Exact Pattern Matching in Genomic Sequences through ReRAM technology
Farzaneh Zokaee, Mingzhe Zhang, Lei Jiang
Genomics is the critical key to enabling precision medicine, ensuring global food security and enforcing wildlife conservation. The massive genomic data produced by various genome…
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…
DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework
Zihao Liu, Tao Liu, Wujie Wen +4
As one of most fascinating machine learning techniques, deep neural network (DNN) has demonstrated excellent performance in various intelligent tasks such as image classification.…
PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic Architecture with Efficient Supervised Learning
Tao Liu, Lei Jiang, Yier Jin +2
One of the most exciting advancements in AI over the last decade is the wide adoption of ANNs, such as DNN and CNN, in many real-world applications. However, the underlying massive…
Performance Characterization of Multi-threaded Graph Processing Applications on Intel Many-Integrated-Core Architecture
Lei Jiang, Langshi Chen, Judy Qiu
Intel Xeon Phi many-integrated-core (MIC) architectures usher in a new era of terascale integration. Among emerging killer applications, parallel graph processing has been a critic…