research-article

Deep Learning with Differential Privacy

Authors:

Ian Goodfellow,

H. Brendan McMahan,

Kunal Talwar, and

Li ZhangAuthors Info & Claims

CCS '16: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

October 2016

Pages 308 - 318

https://doi.org/10.1145/2976749.2978318

Published: 24 October 2016 Publication History

Abstract

Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. The models should not expose private information in these datasets. Addressing this goal, we develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy. Our implementation and experiments demonstrate that we can train deep neural networks with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.

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https://dl.acm.org/doi/10.5555/3635637.3663019
Jin XYao YYu N(2024)Efficient secure aggregation for privacy-preserving federated learning based on secret sharingJUSTC10.52396/JUSTC-2022-011654:1(0104)Online publication date: 2024
https://doi.org/10.52396/JUSTC-2022-0116
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Index Terms

Deep Learning with Differential Privacy
1. Computing methodologies
  1. Machine learning
2. Security and privacy

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Published In

cover image ACM Conferences

CCS '16: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

October 2016

1924 pages

ISBN:9781450341394

DOI:10.1145/2976749

General Chairs:
Edgar Weippl
SBA Research, Austria
,
Stefan Katzenbeisser
TU Darmstadt, CYSEC, Germany
,
Program Chairs:
Christopher Kruegel
University of California, Santa Barbara, USA
,
Andrew Myers
Cornell University, USA
,
Shai Halevi
IBM Research, USA

Copyright © 2016 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

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Published: 24 October 2016

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CCS'16: 2016 ACM SIGSAC Conference on Computer and Communications Security

October 24 - 28, 2016

Vienna, Austria

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CCS '16 Paper Acceptance Rate 137 of 831 submissions, 16%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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CCS '24

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ACM SIGSAC Conference on Computer and Communications Security

October 14 - 18, 2024

Salt Lake City , UT , USA

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https://dl.acm.org/doi/10.5555/3635637.3663019
Jin XYao YYu N(2024)Efficient secure aggregation for privacy-preserving federated learning based on secret sharingJUSTC10.52396/JUSTC-2022-011654:1(0104)Online publication date: 2024
https://doi.org/10.52396/JUSTC-2022-0116
Ben Ammar MGhodhbani RSaidani T(2024)Enhancing Neural Network Resilence against Adversarial Attacks based on FGSM TechniqueEngineering, Technology & Applied Science Research10.48084/etasr.747914:3(14634-14639)Online publication date: 1-Jun-2024
https://doi.org/10.48084/etasr.7479
Yilmaz ECan O(2024)Unveiling Shadows: Harnessing Artificial Intelligence for Insider Threat DetectionEngineering, Technology & Applied Science Research10.48084/etasr.691114:2(13341-13346)Online publication date: 2-Apr-2024
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