17 citations · 36 across the 19 of their papers we have counts for
6 papers · 1 filter
Trusted Hardware Acceleration for Function Secret Sharing
Pengzhi Huang, Kiwan Maeng, G. Edward Suh
Function secret sharing (FSS) is a core building block for privacy-preserving systems such as secure inference and private information retrieval (PIR), but incurs significant overh…
Beyond Latency: A System-Level Characterization of MPC and FHE for PPML
Pengzhi Huang, Kiwan Maeng, G. Edward Suh
Privacy protection has become an increasing concern in modern machine learning applications. Privacy-preserving machine learning (PPML) has attracted growing research attention, wi…
Composition for Pufferfish Privacy
Jiamu Bai, Guanlin He, Xin Gu +2
When creating public data products out of confidential datasets, inferential/posterior-based privacy definitions, such as Pufferfish, provide compelling privacy semantics for data…
CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation
Jinyu Liu, Gang Tan, Kiwan Maeng
MPC-based ML uses multi-party computation (MPC) to run machine learning (ML) workloads across multiple parties without each having to share their private data or model parameters.…
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
Maximilian Lam, Jeff Johnson, Wenjie Xiong +11
On-device machine learning (ML) inference can enable the use of private user data on user devices without revealing them to remote servers. However, a pure on-device solution to pr…
Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information
Kiwan Maeng, Chuan Guo, Sanjay Kariyappa +1
Split learning and inference propose to run training/inference of a large model that is split across client devices and the cloud. However, such a model splitting imposes privacy c…