Enc2: Privacy-Preserving Inference for Tiny IoTs via Encoding and Encryption-Reference-Cited by-同舟云学术

Enc2: Privacy-Preserving Inference for Tiny IoTs via Encoding and Encryption

Published:2023-10-02 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th Annual International Conference on Mobile Computing and Networking
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Author:

Chien Hao-Jen¹^ORCID,Khalili Hossein¹^ORCID,Hass Amin²^ORCID,Sehatbakhsh Nader³^ORCID

Affiliation:

1. UCLA, Los Angeles, CA, USA

2. Accenture, Washington, DC, USA

3. UCLA, Los Angeles, CA, United States

Funder

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3570361.3592501

Reference72 articles.

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3. (Leveled) Fully Homomorphic Encryption without Bootstrapping

4. Alon Brutzkus , Ran Gilad-Bachrach , and Oren Elisha . 2019 . Low latency privacy preserving inference . In International Conference on Machine Learning. PMLR, 812--821 . Alon Brutzkus, Ran Gilad-Bachrach, and Oren Elisha. 2019. Low latency privacy preserving inference. In International Conference on Machine Learning. PMLR, 812--821.

5. Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). Ieee 39--57. Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). Ieee 39--57.