Authors:
Laurent Gomez
1
;
Tianchi Yu
1
and
Patrick Duverger
2
Affiliations:
1
SAP Security Research, SAP Labs France, Mougins, France
;
2
City of Antibes, France
Keyword(s):
Machine Learning, Edge Computing, Intellectual Property, Data Privacy, Privacy Enhancing Technology, Trusted Execution Environment.
Abstract:
With the adoption of IoT-like technologies, industrials aim to enhance the business value of their physical assets and improve their operational efficiency. However, IoT devices alone tend to strain enterprise systems with a sheer volume of unstructured and unfiltered data. To overcome this challenge, endowing (smart) devices with AI-based capabilities can significantly enhance enterprise system capabilities. However, deploying AI-based capabilities on potentially insecure edge hardware and platforms introduces new security risks, including AI model theft, poisoning, and data leaks. This paradigm shift necessitates the protection of distributed AI applications and data. In this paper, we propose a solution for safeguarding the Intellectual Property and data privacy of ML-based software. We utilize hardware-assisted Privacy Enhancing Technologies, specifically Trusted Execution Environments. We evaluate the effectiveness of our approach in the context of ML-based motion detection in C
CTV cameras. This work is part of a co-innovation project with the Smart City of Antibes, France.
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