Preserving Privacy in GANs Against Membership Inference Attack-Reference-Cited by-同舟云学术

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Preserving Privacy in GANs Against Membership Inference Attack

Published:2024 Issue: Volume:19 Page:1728-1743
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Shateri Mohammadhadi¹^ORCID,Messina Francisco²^ORCID,Labeau Fabrice³^ORCID,Piantanida Pablo⁴^ORCID

Affiliation:

1. Department of Systems Engineering, École de technologie supérieure, Montreal, QC, Canada

2. School of Engineering, Universidad de Buenos Aires, Buenos Aires, Argentina

3. Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada

4. International Laboratory on Learning Systems (ILLS) and MILA-Quebec AI Institute, Montreal, QC, Canada

Funder

Hydro-Quebec

Natural Sciences and Engineering Research Council of Canada

McGill University in the framework of the NSERC/Hydro-Quebec Industrial Research Chair in Interactive Information Infrastructure for the Power Grid

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/10206/10319981/10359117.pdf?arnumber=10359117

Reference43 articles.

1. The Long Road to Computational Location Privacy: A Survey

2. Real-Time Privacy-Preserving Data Release for Smart Meters

3. Authenticated Medical Documents Releasing with Privacy Protection and Release Control

4. Defect Image Sample Generation With GAN for Improving Defect Recognition

5. Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN)

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