Secure Aggregation Privacy Analysis in Federated Learning

Core Concepts

Secure aggregation in federated learning lacks strong privacy against membership inference attacks, necessitating additional privacy mechanisms.

Abstract

The content delves into the privacy implications of secure aggregation (SecAgg) in federated learning, highlighting its vulnerability to membership inference attacks. Despite claims of privacy preservation, SecAgg offers weak privacy, especially in high-dimensional models. The analysis reveals the need for supplementary privacy-enhancing mechanisms, such as noise injection, in federated learning. The study includes experiments on the ADULT and EMNIST Digits datasets, showcasing the ineffectiveness of SecAgg in providing robust privacy.

Introduction to Federated Learning

Federated learning allows collaborative model training.
Secure aggregation (SecAgg) masks local updates for privacy.
Prevailing assumptions suggest strong privacy with SecAgg.

Privacy Analysis of SecAgg

Formal analysis reveals weak privacy against attacks.
Membership inference attacks exploit SecAgg vulnerabilities.
Additional mechanisms like noise injection are essential.

Experimental Results

ADULT dataset experiments show high success in attacks.
EMNIST Digits dataset confirms weak privacy with SecAgg.
Privacy auditing highlights the inefficacy of SecAgg.

Conclusion and Recommendations

SecAgg requires additional privacy measures.
High-dimensional models pose privacy challenges.
The study emphasizes the importance of robust privacy mechanisms in federated learning.

Stats

Our numerical results unveil that, contrary to prevailing claims, SecAgg offers weak privacy against membership inference attacks even in a single training round.

Quotes

"Our findings underscore the imperative for additional privacy-enhancing mechanisms in federated learning."

Key Insights Distilled From

Secure Aggregation is Not Private Against Membership Inference Attacks

by Khac... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17775.pdf

Deeper Inquiries

How can federated learning systems enhance privacy beyond secure aggregation

Federated learning systems can enhance privacy beyond secure aggregation by incorporating additional privacy-preserving mechanisms such as differential privacy (DP) and homomorphic encryption. Differential privacy adds noise to the data before sharing it, ensuring that individual data points cannot be distinguished in the aggregated dataset. This helps protect the privacy of individual user data while still allowing for collaborative model training. Homomorphic encryption allows computations to be performed on encrypted data without decrypting it, further safeguarding sensitive information. By combining these techniques with secure aggregation, federated learning systems can achieve a higher level of privacy protection.

What are the implications of weak privacy in high-dimensional models for federated learning

The implications of weak privacy in high-dimensional models for federated learning are significant. In high-dimensional models, where the model size is much larger than the number of clients, the privacy guarantee provided by secure aggregation may be compromised. The study's findings suggest that simply adding independent local updates may not be sufficient to hide individual updates in such scenarios. This weak privacy can lead to increased vulnerability to membership inference attacks, where an adversary can discern sensitive information about individual data points. As a result, the integrity and confidentiality of user data in federated learning systems may be at risk, highlighting the need for stronger privacy-enhancing mechanisms.

How can the study's findings impact the development of privacy-preserving mechanisms in federated learning

The study's findings can have a profound impact on the development of privacy-preserving mechanisms in federated learning. By revealing the limitations of secure aggregation in providing strong privacy guarantees, the research underscores the importance of incorporating additional privacy-enhancing techniques, such as noise injection and differential privacy, in federated learning systems. Developers and researchers can use these insights to design more robust and secure privacy mechanisms that protect user data effectively in high-dimensional models. This can lead to advancements in privacy-preserving federated learning algorithms and protocols, ensuring the confidentiality and integrity of sensitive information in collaborative machine learning settings.