Prediction Privacy in Distributed Multi-Exit Neural Networks: Vulnerabilities and Solutions-Reference-Cited by-同舟云学术

Prediction Privacy in Distributed Multi-Exit Neural Networks: Vulnerabilities and Solutions

Published:2023-11-15 Issue: Volume: Page:
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Container-title:Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security
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Author:

Kannan Tejas¹^ORCID,Feamster Nick¹^ORCID,Hoffmann Henry¹^ORCID

Affiliation:

1. University of Chicago, Chicago, IL, USA

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3576915.3623069

Reference105 articles.

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2. Deep Learning with Differential Privacy

3. Fevzi Alimoglu and Ethem Alpaydin. 1996. Methods of combining multiple classifiers based on different representations for pen-based handwritten digit recognition. In 5th Turkish Artificial Intelligence and Artificial Neural Networks Symposium. Citeseer.

4. Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra Perez, and Jorge Luis Reyes Ortiz. 2013. A public domain dataset for human activity recognition using smartphones. In 21st International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 437--442.

5. Keeping the Smart Home Private with Smart(er) IoT Traffic Shaping

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1. Prediction Privacy in Distributed Multi-Exit Neural Networks: Vulnerabilities and Solutions;Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security;2023-11-15