CHANCEL: Efficient Multi-client Isolation Under Adversarial Programs
Adil M. Ahmad, Juhee Kim, Jaebaek Seo, Insik Shin, Pedro Fonseca, Byoungyoung Lee
Abstract
Intel SGX aims to provide the confidentiality of user data on untrusted cloud machines. However, applications that process confidential user data may contain bugs that leak information or be programmed maliciously to collect user data. Existing research that attempts to solve this problem does not consider multi-client isolation in a single enclave. We show that by not supporting such in-enclave isolation, they incur considerable slowdown when concurrently processing multiple clients in different enclave processes, due to the limitations of SGX.
Topics & Concepts
Isolation (microbiology)Adversarial systemComputer scienceArtificial intelligenceBiologyMicrobiologySecurity and Verification in ComputingAdvanced Malware Detection TechniquesAdversarial Robustness in Machine Learning