Cited by 399Privacy-Preserving Technologies in DataCloud Data Security SolutionsCryptography and Data Security

Privacy preservation in federated learning: An insightful survey from the GDPR perspective

Open access
Truong, Nguyen, Sun, Kai, Wang, Siyao, Guitton, Florian, Guo, YiKe·2021·Computers & Security

Abstract

In recent years, along with the blooming of Machine Learning (ML)-based applications and services, ensuring data privacy and security have become a critical obligation. ML-based service providers not only confront with difficulties in collecting and managing data across heterogeneous sources but also challenges of complying with rigorous data protection regulations such as EU/UK General Data Protection Regulation (GDPR). Furthermore, conventional centralised ML approaches have always come with long-standing privacy risks to personal data leakage, misuse, and abuse. Federated learning (FL) has emerged as a prospective solution that facilitates distributed collaborative learning without disclosing original training data. Unfortunately, retaining data and computation on-device as in FL are not sufficient for privacy-guarantee because model parameters exchanged among participants conceal sensitive information that can be exploited in privacy attacks. Consequently, FL-based systems are not naturally compliant with the GDPR. This article is dedicated to surveying of state-of-the-art privacy-preservation techniques in FL in relations with GDPR requirements. Furthermore, insights into the existing challenges are examined along with the prospective approaches following the GDPR regulatory guidelines that FL-based systems shall implement to fully comply with the GDPR. © 2021

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Crossref · 10.1016/j.cose.2021.102402

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  • Existing one-shot FL methods are mostly impractical or face inherent limitations, such as requiring a public dataset, homogeneous participant models, additional data/model information upload, or unsatisfactory performance.

    “Despite the low communication cost, existing one-shot FL methods are mostly impractical or face inherent limitations, e.g. , a public dataset is required, participants' models are homogeneous, additional data/model information needs to be uploaded, unsatisfactory performance [188], [189], [190].”

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Authors
Truong, N., Sun, K., Wang, S., Guitton, F., & Guo, Y.
Affiliations
  • Imperial College London (GB)
  • Hong Kong Baptist University (HK)
Corresponding author
  • Truong, Nguyen
  • Guo, YiKe
Venue
Computers & Security
Publisher
Elsevier BV
Published
2021-11
Volume
110
Pages
102402
ISSN
0167-4048 (ISSN-L)
OpenAlex
W3185987447
Cited by
399
References
159
Citations by year
2026: 73 · 2025: 118 · 2024: 83 · 2023: 69 · 2022: 44 · 2021: 12
FWCI
18.31
Type
journal article
Language
en
Open access
Locations
  • Computers & Security (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
Imperial College London
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
  • Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
Keywords
federated learning, privacy preservation, GDPR compliance, data protection regulation, privacy attacks, personal data protection, regulatory compliance

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