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  Gradient-Leaks: Understanding Deanonymization in Federated Learning

Orekondy, T., Oh, S. J., Zhang, Y., Schiele, B., & Fritz, M. (in press). Gradient-Leaks: Understanding Deanonymization in Federated Learning. In The 2nd International Workshop on Federated Learning for Data Privacy and Confidentiality. federated-learning.org.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0005-8E47-C Version Permalink: https://hdl.handle.net/21.11116/0000-0008-27D2-F
Genre: Conference Paper
Latex : Gradient-Leaks: {U}nderstanding Deanonymization in Federated Learning

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 Creators:
Orekondy, Tribhuvanesh1, Author           
Oh, Seong Joon1, Author           
Zhang, Yang2, Author
Schiele, Bernt1, Author           
Fritz, Mario2, Author           
Affiliations:
1Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 2019
 Publication Status: Accepted / In Press
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: orekondy19gradient
URN: http://federated-learning.org/fl-neurips-2019/
 Degree: -

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Title: The 2nd International Workshop on Federated Learning for Data Privacy and Confidentiality
Place of Event: Vancouver, Canada
Start-/End Date: 2019-12-13 - 2019-12-13

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Title: The 2nd International Workshop on Federated Learning for Data Privacy and Confidentiality
  Abbreviation : FL-NeurIPS 2019
  Subtitle : (in Conjunction with NeurIPS 2019)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: federated-learning.org
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -