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Smart city article details
| Title | Security For Distributed Machine Learning |
|---|---|
| ID_Doc | 47930 |
| Authors | Gomez L.; Yu T.; Duverger P. |
| Year | 2023 |
| Published | Proceedings of the International Conference on Security and Cryptography, 1 |
| DOI | http://dx.doi.org/10.5220/0012137700003555 |
| 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 CCTV cameras. This work is part of a co-innovation project with the Smart City of Antibes, France. © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0). |
| Author Keywords | Data Privacy; Edge Computing; Intellectual Property; Machine Learning; Privacy Enhancing Technology; Trusted Execution Environment |
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