Efficient and Privacy-Preserving Federated Learning with Full Homomorphic Encryption

Centrala begrepp

Utilizing Full Homomorphic Encryption for Improved Security, Efficiency, and Practicality in Federated Learning.

Sammanfattning

The article discusses the advancements in Federated Learning Schemes using Full Homomorphic Encryption (FHE). It introduces novel schemes that enhance security, functionality, and practicality. By comparing different datasets from various fields like medical, business, biometric, and financial sectors, the study shows significant improvements in security, efficiency, and practicality over traditional federated learning schemes. The use of FHE algorithms allows for direct additions or multiplications on ciphertexts, enhancing the redesign of federated learning frameworks. Additionally, the article highlights the importance of security against quantum computing attacks and gradient attacks provided by FHE algorithms. The efficiency of FHE algorithms has significantly improved in recent years, making them more practical for training modules compared to traditional schemes using Partial Homomorphic Encryption (PHE).

Statistik

Comparisons have been given in four practical data sets separately from medical, business, biometric and financial fields.
Our scheme has achieved a great efficiency improvement in training modules compared with classical schemes using PHE algorithms.
The horizontal logistic regression federated learning model is 9.3 times more efficient than the classical algorithm for training.
The vertical logistic regression federated learning model is 3-3.7 times more efficient than the classical algorithm for training.

Citat

"Our scheme achieves significant improvements in security, efficiency and practicality."
"The FHE algorithm can provide controllable security for models and data."
"Our proposed federated learning scheme based on FHE achieves unification in the training process."

Viktiga insikter från

Efficient and Privacy-Preserving Federated Learning based on Full Homomorphic Encryption

by Yuqi Guo,Lin... på arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11519.pdf

Djupare frågor

How can Federated Learning with SecureBoost based on FHE impact other industries beyond those mentioned

Federated Learning with SecureBoost based on Full Homomorphic Encryption (FHE) can have a significant impact on various industries beyond those mentioned in the context. One industry that could benefit greatly is the telecommunications sector. With the increasing focus on data privacy and security, telecom companies are constantly looking for ways to protect sensitive customer information while still leveraging data for network optimization and service improvements. By implementing FHE-based Federated Learning, telecom companies can collaborate securely to analyze network performance, predict maintenance needs, and optimize resource allocation without compromising individual user data privacy.
Another industry that could be positively impacted is e-commerce. Online retailers collect vast amounts of customer data for personalized marketing strategies and product recommendations. However, ensuring the privacy of this data is crucial to maintain consumer trust. FHE-based Federated Learning can enable collaboration between different e-commerce platforms to analyze trends, improve recommendation algorithms, and enhance customer experiences without exposing individual shopping habits or preferences.
Furthermore, the healthcare industry stands to benefit significantly from FHE-based Federated Learning. Medical research often requires access to large datasets containing sensitive patient information. By utilizing secure collaborative models like SecureBoost based on FHE, researchers from multiple institutions can work together to analyze medical records, identify patterns in disease progression or treatment outcomes, and develop more effective healthcare solutions while protecting patient confidentiality.

What are potential drawbacks or limitations of relying solely on Full Homomorphic Encryption for privacy protection

While Full Homomorphic Encryption (FHE) offers robust protection for sensitive data in privacy-preserving computing scenarios like Federated Learning, there are potential drawbacks and limitations associated with relying solely on this encryption technique:

Computational Overhead: FHE operations are computationally intensive compared to other encryption methods like Partial Homomorphic Encryption (PHE). This high computational overhead can lead to slower processing speeds and increased resource requirements when performing complex computations on encrypted data.

Key Management Complexity: Managing keys in an FHE system can be challenging due to the need for key generation, distribution, storage, and rotation processes. Ensuring secure key management practices adds complexity to the overall system architecture.

Noise Accumulation: In FHE schemes like CKKS used in SecureBoost models discussed above introduce noise during homomorphic operations which may accumulate over multiple computations leading to accuracy degradation if not managed effectively.

4 .Limited Practicality: While advancements have been made in improving efficiency of FHE algorithms over time as seen with CKKS algorithm's efficient numerical operations handling , there are still practical limitations when it comes implementation at scale especially considering real-time applications where low latency is critical.

How might advancements in Private Set Intersection protocols influence future developments in privacy-preserving computing

Advancements in Private Set Intersection (PSI) protocols play a crucial role in enhancing privacy-preserving computing techniques such as federated learning by enabling secure collaboration between parties holding private datasets without revealing raw information about their respective datasets.
Here are some ways PSI protocols advancements might influence future developments:
1 .Improved Privacy Protection: Enhanced PSI protocols offer stronger guarantees of privacy by allowing parties involved in collaborative tasks such as federated learning or multi-party computation share insights derived from their respective datasets without sharing actual raw dataset details thus reducing risks associated with exposure of sensitive information
2 .Reduced Communication Overheads: Efficient PSI protocols help minimize communication overheads during collaborative tasks by enabling parties only exchange necessary cryptographic tokens instead of entire dataset thereby optimizing bandwidth usage .
3 .Scalability & Interoperability: Advancements will likely focus on developing scalable PSI techniques capable handling larger volumes of distributed datasets across diverse systems making it easier integrate into existing infrastructure facilitating seamless interoperability among different platforms
4 .Security Enhancements : Future developments might include incorporating advanced cryptographic primitives into PSI protocols further fortifying security against emerging threats including side-channel attacks , differential attacks etc thus ensuring robust protection mechanisms
These advancements will pave way towards more widespread adoption of federated learning approaches across various domains including healthcare , finance , IoT etc where maintaining strict confidentiality while deriving meaningful insights from distributed sources remains paramount concern