Prive-HD: Privacy-Preserved Hyperdimensional Computing

DAC, pp. 1-6, 2020.

Cited by: 0|Bibtex|Views47|DOI:https://doi.org/10.1109/DAC18072.2020.9218493
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Other Links: arxiv.org|dblp.uni-trier.de|academic.microsoft.com

Abstract:

The privacy of data is a major challenge in machine learning as a trained model may expose sensitive information of the enclosed dataset. Besides, the limited computation capability and capacity of edge devices have made cloud-hosted inference inevitable. Sending private information to remote servers makes the privacy of inference also ...More

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