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Accessibility of covariance information creates vulnerability in Federated Learning frameworks
Manuel Huth1,2, Jonas Arruda2, Roy Gusinow1,2
1Institute of Computational Biology, Helmholtz Munich, Neuherberg 85764, Germany.
Bioinformatics (Oxford, England)
|August 30, 2023
Summary
A new attack can reconstruct private data in Federated Learning (FL) systems, even with noise defenses. This method exploits basic data functionalities, highlighting vulnerabilities in current FL frameworks.
Area of Science:
- Computer Science
- Cybersecurity
- Machine Learning
Background:
- Federated Learning (FL) enables collaborative data analysis without centralizing sensitive information, particularly in healthcare.
- Despite its privacy benefits, FL systems are vulnerable to data leakage attacks.
- Existing FL frameworks may not adequately protect against sophisticated privacy breaches.
Purpose of the Study:
- To introduce a novel attack algorithm for data reconstruction in Federated Learning.
- To assess the effectiveness of this attack against common defense strategies.
- To identify limitations in current FL frameworks and propose improvements.
Main Methods:
- Developed a novel attack algorithm utilizing sample means, covariances, and linearly independent vectors.
- Evaluated the attack's robustness against random noise-based defense mechanisms.
- Analyzed the implications of differential privacy as a potential defense strategy.
Main Results:
- Demonstrated that basic data functionalities in FL are sufficient for data reconstruction.
- The proposed attack is resilient to random noise commonly used for privacy protection.
- Identified significant limitations in the privacy guarantees of established FL frameworks.
Conclusions:
- Current Federated Learning frameworks are susceptible to data leakage via covariance-based attacks.
- Existing defense mechanisms, including random noise, offer insufficient protection.
- Further research into robust defense strategies, potentially involving differential privacy, is crucial for secure FL implementation.
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