GuardianML: Anatomy of Privacy-Preserving Machine Learning Techniques and Frameworks

GuardianML: Anatomy of Privacy-Preserving Machine Learning Techniques and Frameworks

Machine learning has become integral to our lives, finding applications in nearly every aspect of our daily routines. However, using personal information in machine learning applications has raised concerns about user data privacy and security. As concerns about data privacy grow, algorithms and tec...

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Bibliographic Details
Main Authors: Nges Brian Njungle, Eric Jahns, Zhenqi Wu, Luigi Mastromauro, Milan Stojkov, Michel A. Kinsy
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Differential privacy
federated learning
homomorphic encryption
multi-party computation
privacy preserving machine learning
trusted execution environment
Online Access:https://ieeexplore.ieee.org/document/10947759/
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https://ieeexplore.ieee.org/document/10947759/

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