research-article

Closing the Loophole: Rethinking Reconstruction Attacks in Federated Learning from a Privacy Standpoint

Authors:

Hyeong Gwon Hong,

Seungwon ShinAuthors Info & Claims

ACSAC '22: Proceedings of the 38th Annual Computer Security Applications Conference

Pages 332 - 345

https://doi.org/10.1145/3564625.3564657

Published: 05 December 2022 Publication History

Abstract

Federated Learning was deemed as a private distributed learning framework due to the separation of data from the central server. However, recent works have shown that privacy attacks can extract various forms of private information from legacy federated learning. Previous literature describe differential privacy to be effective against membership inference attacks and attribute inference attacks, but our experiments show them to be vulnerable against reconstruction attacks. To understand this outcome, we execute a systematic study of privacy attacks from the standpoint of privacy. The privacy characteristics that reconstruction attacks infringe are different from other privacy attacks, and we suggest that privacy breach occurred at different levels. From our study, reconstruction attack defense methods entail heavy computation or communication costs. To this end, we propose Fragmented Federated Learning (FFL), a lightweight solution against reconstruction attacks. This framework utilizes a simple yet novel gradient obscuring algorithm based on a newly proposed concept called the global gradient and determines which layers are safe for submission to the server. We show empirically in diverse settings that our framework improves practical data privacy of clients in federated learning with an acceptable performance trade-off without increasing communication cost. We aim to provide a new perspective to privacy in federated learning and hope this privacy differentiation can improve future privacy-preserving methods.

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Cited By

Allaart CAmiri SBal HBelloum AGommans Lvan Halteren AKlous S(2024)Private and Secure Distributed Deep Learning: A SurveyACM Computing Surveys10.1145/370345257:4(1-43)Online publication date: 15-Nov-2024
https://dl.acm.org/doi/10.1145/3703452
ElZemity AArief B(2024)Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics62450.2024.00072(331-338)Online publication date: 19-Aug-2024
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Kim JSong MSeo MJin YShin S(2024)PassREfinder: Credential Stuffing Risk Prediction by Representing Password Reuse between Websites on a Graph2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00020(1385-1404)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00020
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Index Terms

Closing the Loophole: Rethinking Reconstruction Attacks in Federated Learning from a Privacy Standpoint
1. Computing methodologies
  1. Machine learning
2. Security and privacy

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ACSAC '22: Proceedings of the 38th Annual Computer Security Applications Conference

December 2022

1021 pages

ISBN:9781450397599

DOI:10.1145/3564625

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Published: 05 December 2022

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ACSAC: Annual Computer Security Applications Conference

December 5 - 9, 2022

TX, Austin, USA

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Cited By

Allaart CAmiri SBal HBelloum AGommans Lvan Halteren AKlous S(2024)Private and Secure Distributed Deep Learning: A SurveyACM Computing Surveys10.1145/370345257:4(1-43)Online publication date: 15-Nov-2024
https://dl.acm.org/doi/10.1145/3703452
ElZemity AArief B(2024)Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics62450.2024.00072(331-338)Online publication date: 19-Aug-2024
https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics62450.2024.00072
Kim JSong MSeo MJin YShin S(2024)PassREfinder: Credential Stuffing Risk Prediction by Representing Password Reuse between Websites on a Graph2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00020(1385-1404)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00020
Xiao DYang DLi JChen XWu W(2024)Privacy Leakage from Logits Attack and its Defense in Federated Distillation2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)10.1109/DSN58291.2024.00029(169-182)Online publication date: 24-Jun-2024
https://doi.org/10.1109/DSN58291.2024.00029
Na SKim KShin S(2023)Witnessing Erosion of Membership Inference Defenses: Understanding Effects of Data Drift in Membership PrivacyProceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3607199.3607224(250-263)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3607199.3607224

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