SPOT: Structure Patching and Overlap Tweaking for Effective Pipelining in Privacy-Preserving MLaaS with Tiny Clients

Machine Learning as a Service (MLaaS) has paved the way for numerous applications for resource-limited clients, such as IoT/mobile users. However, it raises a great challenge for privacy, including both the data privacy of clients and model privacy of the server. While there have been extensive stud...

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Veröffentlicht in:	Proceedings of the International Conference on Distributed Computing Systems S. 1318 - 1329
Hauptverfasser:	Xu, Xiangrui, Zhang, Qiao, Ning, Rui, Xin, Chunsheng, Wu, Hongyi
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 23.07.2024
Schlagworte:	Computational efficiency Data privacy Encryption Homomorphic Encryption Machine learning Machine Learning as a Service Memory management Mobile Computing Mobile handsets Neural networks Privacy-preserving Structure Patching
ISSN:	2575-8411
Online-Zugang:	Volltext
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