TY - GEN
T1 - Performance Acceleration of Secure Machine Learning Computations for Edge Applications
AU - Lin, Zi Jie
AU - Wang, Chuan Chi
AU - Tu, Chia Heng
AU - Hung, Shih Hao
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Edge appliances built with machine learning applications have been gradually adopted in a wide variety of application fields, such as intelligent transportation, the banking industry, and medical diagnosis. Privacy-preserving computation approaches can be used on smart appliances in order to secure the privacy of sensitive data, including application data and the parameters of machine learning models. Nevertheless, the data privacy is achieved at the cost of execution time. That is, the execution speed of a secure machine learning application is several orders of magnitude slower than that of the application in plaintext. Especially, the performance gap is enlarged for edge appliances. In this work, in order to improve the execution efficiency of secure applications, an open-source software framework CrypTen is targeted, which is widely used for building secure machine learning applications using the Secure Multi-Party Computation (SMPC) based privacy-preserving computation approach. We analyze the performance characteristics of the secure machine learning applications built with CrypTen, and the analysis reveals that the communication overhead hinders the execution of the secure applications. To tackle the issue, a communication library, OpenMPI, is added to the CrypTen framework as a new communication backend to boost the application performance by up to 50%. We further develop a hybrid communication scheme by combining the OpenMPI backend with the original communication backend with the CrypTen framework. The experimental results show that the enhanced CrypTen framework is able to provide better performance for the small-size data (LeNet5 on MNIST dataset by up to 50% of speedup) and maintain similar performance for large-size data (AlexNet on CIFAR-10), compared to the original CrypTen framework.
AB - Edge appliances built with machine learning applications have been gradually adopted in a wide variety of application fields, such as intelligent transportation, the banking industry, and medical diagnosis. Privacy-preserving computation approaches can be used on smart appliances in order to secure the privacy of sensitive data, including application data and the parameters of machine learning models. Nevertheless, the data privacy is achieved at the cost of execution time. That is, the execution speed of a secure machine learning application is several orders of magnitude slower than that of the application in plaintext. Especially, the performance gap is enlarged for edge appliances. In this work, in order to improve the execution efficiency of secure applications, an open-source software framework CrypTen is targeted, which is widely used for building secure machine learning applications using the Secure Multi-Party Computation (SMPC) based privacy-preserving computation approach. We analyze the performance characteristics of the secure machine learning applications built with CrypTen, and the analysis reveals that the communication overhead hinders the execution of the secure applications. To tackle the issue, a communication library, OpenMPI, is added to the CrypTen framework as a new communication backend to boost the application performance by up to 50%. We further develop a hybrid communication scheme by combining the OpenMPI backend with the original communication backend with the CrypTen framework. The experimental results show that the enhanced CrypTen framework is able to provide better performance for the small-size data (LeNet5 on MNIST dataset by up to 50% of speedup) and maintain similar performance for large-size data (AlexNet on CIFAR-10), compared to the original CrypTen framework.
UR - http://www.scopus.com/inward/record.url?scp=85142089529&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142089529&partnerID=8YFLogxK
U2 - 10.1109/RTCSA55878.2022.00021
DO - 10.1109/RTCSA55878.2022.00021
M3 - Conference contribution
AN - SCOPUS:85142089529
T3 - Proceedings - 2022 IEEE 28th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2022
SP - 138
EP - 147
BT - Proceedings - 2022 IEEE 28th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2022
Y2 - 23 August 2022 through 25 August 2022
ER -