14 citations · 19 across the 4 of their papers we have counts for
8 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 (…
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…
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…
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…
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…