activity
20182026
most citedPrivFT: Private and Fast Text Classification with Homomorphic Encryption

96 citations · 133 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CR2026

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…

cs.CR2026

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 (…

cs.LG2022★ 2 cited

Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy

Ergute Bao, Yizheng Zhu, Xiaokui Xiao +4

Deep neural networks have strong capabilities of memorizing the underlying training data, which can be a serious privacy concern. An effective solution to this problem is to train…

cs.CR2021★ 5 cited

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…

cs.CR2021

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…

cs.CR2019★ 96 cited

PrivFT: Private and Fast Text Classification with Homomorphic Encryption

Ahmad Al Badawi, Luong Hoang, Chan Fook Mun +2

The need for privacy-preserving analytics is higher than ever due to the severity of privacy risks and to comply with new privacy regulations leading to an amplified interest in pr…

stat.AP2019

Achieving GWAS with Homomorphic Encryption

Jun Jie Sim, Fook Mun Chan, Shibin Chen +2

One way of investigating how genes affect human traits would be with a genome-wide association study (GWAS). Genetic markers, known as single-nucleotide polymorphism (SNP), are use…

cs.CR2019★ 14 cited

CaRENets: Compact and Resource-Efficient CNN for Homomorphic Inference on Encrypted Medical Images

Jin Chao, Ahmad Al Badawi, Balagopal Unnikrishnan +9

Convolutional neural networks (CNNs) have enabled significant performance leaps in medical image classification tasks. However, translating neural network models for clinical appli…

cs.CR2018★ 16 cited

Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs

Ahmad Al Badawi, Jin Chao, Jie Lin +6

Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based inference applications. In this setting, the cloud has a pre-learned model whereas the user has sa…