аналитика - Machine Learning - # Privacy-Preserving Federated Learning

FheFL: A Novel Federated Learning Algorithm for Privacy and Security Using Fully Homomorphic Encryption

Q: How does the computational cost of FheFL compare to other privacy-preserving federated learning approaches, particularly those based on differential privacy or secure multi-party computation?

FheFL, being based on Fully Homomorphic Encryption (FHE), inherently carries a higher computational cost compared to Differential Privacy (DP) or some Secure Multi-party Computation (SMPC) techniques. Here's a breakdown: FheFL (FHE-based): FHE operations are computationally intensive due to the complex mathematical operations on encrypted data. This leads to higher computation times for both users (during encryption) and the server (during aggregation and decryption). The use of the CKKS scheme and techniques like SIMD in FheFL helps improve efficiency but doesn't eliminate the inherent overhead of FHE. Differential Privacy (DP): DP involves adding noise to the data, which is computationally lightweight. This makes DP approaches significantly faster than FHE-based methods. However, the trade-off is a potential reduction in accuracy, especially with stronger privacy guarantees (lower epsilon values). Secure Multi-party Computation (SMPC): The computational cost of SMPC varies greatly depending on the specific protocol used. Some SMPC protocols can be more efficient than FHE, especially for simpler computations. However, complex protocols or those requiring extensive communication between users can become computationally expensive. In summary: Computational Cost: DP SMPC > DP (trade-off with privacy) FheFL's advantage lies in its ability to provide strong privacy and security guarantees without relying on weak assumptions about user collusion or requiring trusted third parties. This comes at the cost of higher computational overhead compared to DP or some SMPC approaches.

Q: Could the non-poisoning rate-based aggregation scheme be adapted to other types of attacks beyond data poisoning, such as model poisoning or adversarial examples?

While primarily designed for data poisoning attacks, the non-poisoning rate-based aggregation scheme in FheFL has potential adaptability to other attack vectors, albeit with modifications: Model Poisoning: Challenge: Model poisoning involves malicious users sending corrupted model updates (weights) directly, rather than manipulated data. The current distance-based metric might not directly detect subtly crafted malicious updates. Adaptation: Instead of comparing against the previous global model, the scheme could compare each user's update against a robust aggregate of a subset of users (e.g., using techniques like Byzantine-robust aggregation). This would require careful selection of the subset and potential modifications to the weighting scheme. Adversarial Examples: Challenge: Adversarial examples are crafted inputs designed to fool a trained model. Detecting them during the training phase is difficult as they don't necessarily manifest as large deviations in model updates. Adaptation: Integrating techniques like adversarial training within the local training process of each user could be more effective. This involves users generating and training on their own adversarial examples, making the global model more robust. The non-poisoning rate scheme might not be directly applicable here. Key Considerations for Adaptation: Nature of the Attack: Understanding the specific characteristics of the attack is crucial for adapting the scheme effectively. Computational Overhead: Modifications should consider the computational constraints of FHE and aim to minimize additional overhead. False Positives: The adapted scheme should minimize falsely flagging benign updates as malicious, which could hinder model convergence.

Основные понятия

FheFL is a new federated learning algorithm that uses fully homomorphic encryption (FHE) and a novel aggregation scheme based on users' non-poisoning rates to address both privacy and security concerns in federated learning environments.

Аннотация

Bibliographic Information

Rahulamathavan, Y., Herath, C., Liu, X., Lambotharan, S., & Maple, C. (2024). FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users. arXiv preprint arXiv:2306.05112v3.

Research Objective

This paper introduces FheFL, a novel federated learning algorithm designed to address both privacy and security vulnerabilities inherent in traditional federated learning approaches.

Methodology

FheFL leverages a modified CKKS fully homomorphic encryption scheme to enable secure aggregation of user model updates without compromising individual user data privacy. It introduces a distributed multi-key additive homomorphic encryption scheme and a non-poisoning rate-based aggregation scheme to detect and mitigate data poisoning attacks within the encrypted domain.

Key Findings

FheFL effectively prevents privacy leakage by encrypting gradients, preventing the server from inferring sensitive information.
The non-poisoning rate-based aggregation scheme successfully mitigates data poisoning attacks without requiring access to plaintext model updates.
FheFL achieves comparable accuracy to existing federated learning approaches while maintaining privacy and security.

Main Conclusions

FheFL offers a robust solution for privacy-preserving federated learning by effectively addressing both privacy and security concerns through the innovative use of fully homomorphic encryption and a novel aggregation scheme.

Significance

This research significantly contributes to the field of secure and privacy-preserving machine learning by providing a practical and efficient solution for federated learning in the presence of malicious users.

Limitations and Future Research

The paper acknowledges the computational complexity of FHE and suggests exploring optimizations for practical deployment in resource-constrained environments as an area for future research.

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Статистика

Assumes no more than 20% of participants in the system are attackers.
Requires at least two non-colluding users to ensure data privacy.

Цитаты

Ключевые выводы из

FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users

by Yogachandran... в arxiv.org 10-08-2024

https://arxiv.org/pdf/2306.05112.pdf

FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users

Дополнительные вопросы

How does the computational cost of FheFL compare to other privacy-preserving federated learning approaches, particularly those based on differential privacy or secure multi-party computation?

FheFL, being based on Fully Homomorphic Encryption (FHE), inherently carries a higher computational cost compared to Differential Privacy (DP) or some Secure Multi-party Computation (SMPC) techniques. Here's a breakdown:

FheFL (FHE-based): FHE operations are computationally intensive due to the complex mathematical operations on encrypted data. This leads to higher computation times for both users (during encryption) and the server (during aggregation and decryption). The use of the CKKS scheme and techniques like SIMD in FheFL helps improve efficiency but doesn't eliminate the inherent overhead of FHE.

Differential Privacy (DP): DP involves adding noise to the data, which is computationally lightweight. This makes DP approaches significantly faster than FHE-based methods. However, the trade-off is a potential reduction in accuracy, especially with stronger privacy guarantees (lower epsilon values).

Secure Multi-party Computation (SMPC): The computational cost of SMPC varies greatly depending on the specific protocol used. Some SMPC protocols can be more efficient than FHE, especially for simpler computations. However, complex protocols or those requiring extensive communication between users can become computationally expensive.
In summary:

Computational Cost: DP < Some SMPC < FHE (FheFL)
Accuracy: FHE (FheFL) > SMPC > DP (trade-off with privacy)
FheFL's advantage lies in its ability to provide strong privacy and security guarantees without relying on weak assumptions about user collusion or requiring trusted third parties. This comes at the cost of higher computational overhead compared to DP or some SMPC approaches.

Could the non-poisoning rate-based aggregation scheme be adapted to other types of attacks beyond data poisoning, such as model poisoning or adversarial examples?

While primarily designed for data poisoning attacks, the non-poisoning rate-based aggregation scheme in FheFL has potential adaptability to other attack vectors, albeit with modifications:

Model Poisoning:

Challenge: Model poisoning involves malicious users sending corrupted model updates (weights) directly, rather than manipulated data. The current distance-based metric might not directly detect subtly crafted malicious updates.
Adaptation: Instead of comparing against the previous global model, the scheme could compare each user's update against a robust aggregate of a subset of users (e.g., using techniques like Byzantine-robust aggregation). This would require careful selection of the subset and potential modifications to the weighting scheme.

Adversarial Examples:

Challenge: Adversarial examples are crafted inputs designed to fool a trained model. Detecting them during the training phase is difficult as they don't necessarily manifest as large deviations in model updates.
Adaptation:  Integrating techniques like adversarial training within the local training process of each user could be more effective. This involves users generating and training on their own adversarial examples, making the global model more robust. The non-poisoning rate scheme might not be directly applicable here.
Key Considerations for Adaptation:

Nature of the Attack: Understanding the specific characteristics of the attack is crucial for adapting the scheme effectively.
Computational Overhead:  Modifications should consider the computational constraints of FHE and aim to minimize additional overhead.
False Positives:  The adapted scheme should minimize falsely flagging benign updates as malicious, which could hinder model convergence.

What are the potential implications of FheFL for enabling secure and privacy-preserving machine learning in other domains beyond federated learning, such as healthcare or finance?

FheFL's principles have significant implications for secure and privacy-preserving machine learning beyond federated learning, particularly in sensitive domains like healthcare and finance:
Healthcare:

Genomic Data Analysis:  FheFL could enable collaborative analysis of sensitive genomic data from multiple institutions without sharing raw data. This could accelerate research on personalized medicine while preserving patient privacy.
Drug Discovery: Pharmaceutical companies could leverage FheFL to train models on combined datasets of clinical trials without exposing confidential patient information, potentially leading to faster drug development.
Medical Image Analysis: Hospitals could collaboratively train models on medical images (e.g., X-rays, MRIs) to improve diagnostic accuracy without compromising patient data privacy.
Finance:

Fraud Detection:  Banks could jointly train models on transaction data to detect fraudulent activities more effectively. FheFL would allow them to do so without sharing sensitive customer financial information.
Risk Assessment: Financial institutions could collaborate on credit risk models using encrypted data from different sources, leading to more accurate risk assessments while complying with privacy regulations.
Algorithmic Trading: Hedge funds could leverage FheFL to train trading algorithms on combined datasets of market data without revealing their proprietary strategies.
Key Advantages of FheFL in these domains:

Strong Privacy Guarantees: FHE ensures that sensitive data remains encrypted throughout the entire machine learning process, mitigating the risk of data breaches and privacy violations.
Compliance with Regulations: FheFL can help organizations comply with stringent data privacy regulations like HIPAA (healthcare) and GDPR (general data protection), enabling secure data utilization.
Collaboration and Data Utilization: FheFL facilitates secure collaboration and data sharing for machine learning, unlocking the potential of larger, more diverse datasets while preserving privacy.
Challenges and Considerations:

Computational Cost: The high computational cost of FHE remains a barrier, especially for resource-constrained devices or large-scale datasets. Advancements in FHE hardware and algorithm optimization are crucial for wider adoption.
Data Utility and Accuracy:  Balancing privacy preservation with data utility and model accuracy requires careful parameter tuning and algorithm design.
Standardization and Interoperability:  Developing standardized protocols and frameworks for FheFL-based systems is essential for seamless integration and interoperability across different institutions.