TY - JOUR
T1 - SGX-Stream
T2 - a secure stream analytics framework In SGX-enabled edge cloud
AU - Bagher, Kassem
AU - Lai, Shangqi
N1 - Funding Information:
The authors would like to acknowledge the financial support of King Abdulaziz University and Monash FIT ECR Seed Grant Scheme for this work and the anonymous reviewers for their valuable comments and constructive suggestions.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2023/2
Y1 - 2023/2
N2 - This paper introduces SGX-Stream, a secure and efficient data analytics framework for data streams using Intel SGX. SGX-Stream employs sketch algorithms in a cloud–edge architecture. To ensure performance and security, SGX-Stream preprocesses the data at the edge of the network to generate sketches and send them to the cloud for further processing inside the SGX enclave. To prioritise urgent tasks, SGX-Stream develops a hybrid task-aware scheduler tailored for SGX to manage task execution securely and practically. SGX-Stream is implemented as a full-fledged framework within the enclave with a small TCB size of 6 kLoC. With an extensible interface, SGX-Stream facilitates the development of various applications, such as adversarial attack detection over data stream. Under different workloads, SGX-Stream can bring 14× speedup on urgent tasks with less than 800 KB of scheduling memory consumption. We also demonstrate SGX-Stream's practicality with three real-world applications.
AB - This paper introduces SGX-Stream, a secure and efficient data analytics framework for data streams using Intel SGX. SGX-Stream employs sketch algorithms in a cloud–edge architecture. To ensure performance and security, SGX-Stream preprocesses the data at the edge of the network to generate sketches and send them to the cloud for further processing inside the SGX enclave. To prioritise urgent tasks, SGX-Stream develops a hybrid task-aware scheduler tailored for SGX to manage task execution securely and practically. SGX-Stream is implemented as a full-fledged framework within the enclave with a small TCB size of 6 kLoC. With an extensible interface, SGX-Stream facilitates the development of various applications, such as adversarial attack detection over data stream. Under different workloads, SGX-Stream can bring 14× speedup on urgent tasks with less than 800 KB of scheduling memory consumption. We also demonstrate SGX-Stream's practicality with three real-world applications.
UR - http://www.scopus.com/inward/record.url?scp=85144410996&partnerID=8YFLogxK
U2 - 10.1016/j.jisa.2022.103403
DO - 10.1016/j.jisa.2022.103403
M3 - Article
AN - SCOPUS:85144410996
SN - 2214-2134
VL - 72
JO - Journal of Information Security and Applications
JF - Journal of Information Security and Applications
M1 - 103403
ER -