SODA: Protecting Proprietary Information in On-Device Machine Learning Models-Reference-Cited by-同舟云学术

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SODA: Protecting Proprietary Information in On-Device Machine Learning Models

Published:2023-12-06 Issue: Volume: Page:121-132
ISSN:
Container-title:Proceedings of the Eighth ACM/IEEE Symposium on Edge Computing
language:
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Author:

Atrey Akanksha¹^ORCID,Sinha Ritwik²^ORCID,Mitra Saayan²^ORCID,Shenoy Prashant¹^ORCID

Affiliation:

1. University of Massachusetts Amherst, Amherst, Massachusetts, USA

2. Adobe Research, San Jose, California, USA

Funder

National Science Foundation

Army Research Laboratory

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583740.3626617

Reference35 articles.

1. 2017. Apple's 'Neural Engine' Infuses the iPhone With AI Smarts. www.wired.com/story/apples-neural-engine-infuses-the-iphone-with-ai-smarts/.

2. 2019. Intel Vision Accelerator Design With Intel Movidius Vision Processing Unit (VPU). https://software.intel.com/en-us/iot/hardware/vision-accelerator-movidius-vpu#specifications.

3. 2019. The NVIDIA EGX Platform for Edge Computing. https://www.nvidia.com/en-us/data-center/products/egx-edge-computing/.

4. 2019. Open Neural Network Exchange. https://onnx.ai/. Accessed on 01/18/2022.

5. Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz, et al. 2013. A public domain dataset for human activity recognition using smartphones.. In Esann.

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