5 citations · 6 across the 5 of their papers we have counts for
6 papers
FESC: Remodeling Long-Context Private Inference with Encrypted State-Space Models
Yufan Zhu, Chao Jin, Khin Mi Mi Aung +1
Processing long, sensitive documents with machine-learning models requires efficient, privacy-preserving long-context inference. Prior private inference systems optimize or distrib…
EncFormer: Secure and Efficient Transformer Inference over Encrypted Data
Yufan Zhu, Chao Jin, Khin Mi Mi Aung +1
Transformer inference in machine-learning-as-a-service (MLaaS) raises privacy concerns for sensitive user inputs. Prior secure solutions that combine fully homomorphic encryption (…
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks
Xue Geng, Zhe Wang, Chunyun Chen +10
Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI) tasks. However, deploying them brings significant challenges due to the huge cost of memory,…
Popcorn: Paillier Meets Compression For Efficient Oblivious Neural Network Inference
Jun Wang, Chao Jin, Souhail Meftah +1
Oblivious inference enables the cloud to provide neural network inference-as-a-service (NN-IaaS), whilst neither disclosing the client data nor revealing the server's model. Howeve…
FFConv: Fast Factorized Convolutional Neural Network Inference on Encrypted Data
Yuxiao Lu, Jie Lin, Chao Jin +4
Homomorphic Encryption (HE), allowing computations on encrypted data (ciphertext) without decrypting it first, enables secure but prohibitively slow Convolutional Neural Network (C…
Parity Declustering for Fault-Tolerant Storage Systems via -designs
Son Hoang Dau, Yan Jia, Chao Jin +2
Parity declustering allows faster reconstruction of a disk array when some disk fails. Moreover, it guarantees uniform reconstruction workload on all surviving disks. It has been s…