Kyung‐Tae Kim,Chung Hwan Kim,Junghwan Rhee,Xiao Yu,Haifeng Chen,Dave Tian,Byoungyoung Lee
标识
DOI:10.1145/3419111.3421282
摘要
Deep learning systems on the cloud are increasingly targeted by attacks that attempt to steal sensitive data. Intel SGX has been proven effective to protect the confidentiality and integrity of such data during computation. However, state-of-the-art SGX systems still suffer from substantial performance overhead induced by the limited physical memory of SGX. This limitation significantly undermines the usability of deep learning systems due to their memory-intensive characteristics.