survey

When Federated Learning Meets Privacy-Preserving Computation

Authors:

Shui YuAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 12

Article No.: 319, Pages 1 - 36

https://doi.org/10.1145/3679013

Published: 03 October 2024 Publication History

Abstract

Nowadays, with the development of artificial intelligence (AI), privacy issues attract wide attention from society and individuals. It is desirable to make the data available but invisible, i.e., to realize data analysis and calculation without disclosing the data to unauthorized entities. Federated learning (FL) has emerged as a promising privacy-preserving computation method for AI. However, new privacy issues have arisen in FL-based application, because various inference attacks can still infer relevant information about the raw data from local models or gradients. This will directly lead to the privacy disclosure. Therefore, it is critical to resist these attacks to achieve complete privacy-preserving computation. In light of the overwhelming variety and a multitude of privacy-preserving computation protocols, we survey these protocols from a series of perspectives to supply better comprehension for researchers and scholars. Concretely, the classification of attacks is discussed, including four kinds of inference attacks as well as malicious server and poisoning attack. Besides, this article systematically captures the state-of-the-art of privacy-preserving computation protocols by analyzing the design rationale, reproducing the experiment of classic schemes, and evaluating all discussed protocols in terms of efficiency and security properties. Finally, this survey identifies a number of interesting future directions.

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Index Terms

When Federated Learning Meets Privacy-Preserving Computation
1. Computing methodologies
  1. Artificial intelligence
2. Security and privacy
  1. Cryptography

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 12

December 2024

966 pages

EISSN:1557-7341

DOI:10.1145/3613718

Editors:
David Atienza
Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
,
Michela Milano
University of Bologna, Italy

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Publication History

Published: 03 October 2024

Online AM: 22 July 2024

Accepted: 05 July 2024

Revised: 15 June 2024

Received: 16 July 2023

Published in CSUR Volume 56, Issue 12

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Zhan ZZhou SLi MZhang R(2025)RAMIE: retrieval-augmented multi-task information extraction with large language models on dietary supplementsJournal of the American Medical Informatics Association10.1093/jamia/ocaf002Online publication date: 11-Jan-2025
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