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Towards Data Science
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Algorithm Protection in the Context of Federated Learning
- The article discusses algorithm protection measures in the context of federated learning, focusing on three critical layers.
- Two categories of protection measures have been identified after analyzing risks.
- Consideration of qualitative and quantitative characteristics of mechanisms was emphasized for selecting suitable solutions.
- Confidential Containers (CoCo) technology aims to protect algorithm code and data from hosting companies using various hardware technologies.
- TEEs, such as CoCo, face security gaps that allow skilled administrators to bypass protection mechanisms, leading to ongoing security concerns.
- Distroless container images reduce attack surfaces but do not protect algorithm code adequately.
- Compiled languages like C, C++, and Rust provide better protection against reverse engineering than interpreted languages like Python.
- Homomorphic Encryption (HE) is highlighted for securing data in federated learning but does not protect algorithms.
- The article emphasizes the importance of hardware isolation for protecting algorithms.
- Combining protection mechanisms like compilation, obfuscation, and encryption creates barriers against intellectual property theft.
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