Efficient Homomorphic Encryption Packing for Vertical Federated Learning

Core Concepts

PackVFL, an efficient vertical federated learning framework based on packed homomorphic encryption, accelerates existing homomorphic encryption-based vertical federated learning algorithms by designing a high-performant matrix multiplication method tailored for the vertical federated learning scenario.

Abstract

The paper proposes PackVFL, an efficient vertical federated learning (VFL) framework based on packed homomorphic encryption (PackedHE), to accelerate existing homomorphic encryption (HE)-based VFL algorithms. The key focus is on designing a high-performant matrix multiplication (MatMult) method, as it dominates the ciphertext computation time in HE-based VFL.
The authors first provide a systematic exploration of the design space for PackedHE MatMult methods, dividing them into slot packing and coefficient packing approaches. They then summarize three key characteristics of VFL's MatMult operation and design a hybrid MatMult method accordingly:

Geo-Distributed Operand: The two operands are owned by geo-distributed parties in VFL. The authors choose the diagonal method as the basic component to reduce communication complexity.

Wide-Range Operand Size: VFL often has varying batch sizes or feature dimensions, leading to varying operand sizes of MatMult. The authors design input packing and partitioning techniques to handle small and large operands efficiently.

Passive Decryption: The resulting ciphertexts of MatMult are transmitted to the party owning the secret key for pure decryption without extra operations. The authors design a lazy rotate-and-sum mechanism to eliminate the remaining time-consuming ciphertext operations.

Furthermore, the authors adaptively apply the proposed MatMult method to three representative HE-based VFL algorithms (VFL-LinR, CAESAR, VFL-NN) by leveraging their distinctive algorithmic properties to further improve efficiency. This includes mechanisms like multiplication level reduction, cleartext inverse rotate-and-sum, and transposed matrices' diagonal conversion.
Empirically, PackVFL achieves up to a 51.52x end-to-end speedup over existing HE-based VFL algorithms, representing a substantial 34.51x greater speedup compared to the direct application of state-of-the-art MatMult methods.

Stats

MatMult gradually occupies up to 99.23% of the cryptographic computation time in the VFL-LinR algorithm.
PackVFL achieves up to a 51.52x end-to-end speedup over existing HE-based VFL algorithms.
PackVFL achieves a 34.51x greater speedup compared to the direct application of state-of-the-art MatMult methods.

Quotes

"PackVFL stands as one of the pioneering works to demonstrate the superiority of PackedHE over Paillier for VFL."
"Our proposed MatMult method exhibits potential advantages over state-of-the-art slot packing approaches, not only in VFL but also in other related domains such as secure model inference."

Key Insights Distilled From

PackVFL: Efficient HE Packing for Vertical Federated Learning

by Liu Yang,Shu... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00482.pdf

Deeper Inquiries

How can PackVFL's techniques be extended to other secure distributed machine learning scenarios beyond vertical federated learning

PackVFL's techniques can be extended to other secure distributed machine learning scenarios beyond vertical federated learning by adapting the principles of PackedHE and the hybrid MatMult method to suit the specific requirements of different applications. For instance, in scenarios where multiple parties need to collaborate on training machine learning models without sharing sensitive data, PackVFL's approach of packing multiple cleartexts into one ciphertext and supporting SIMD-style parallelism can be applied. The design of a high-performant MatMult method tailored to the unique characteristics of the specific scenario can enhance efficiency and security in various distributed machine learning settings. By systematically exploring the design space and adapting the techniques to different use cases, PackVFL's innovations can be leveraged to improve the performance of secure distributed machine learning in a broader context.

What are the potential limitations or drawbacks of the PackedHE-based approach compared to other privacy-preserving techniques like differential privacy or secure multi-party computation

While PackedHE-based approaches, such as PackVFL, offer advantages in terms of efficiency and performance for secure distributed machine learning, they may have potential limitations compared to other privacy-preserving techniques like differential privacy or secure multi-party computation. Some of the drawbacks of PackedHE-based approaches include:

Communication Overhead: PackedHE methods may require significant communication overhead due to the need to transmit ciphertexts between parties for computation, especially in scenarios with large batch sizes or feature dimensions.
Complexity: Designing efficient PackedHE algorithms, such as the MatMult method in PackVFL, can be complex and require domain-specific knowledge. This complexity may limit the applicability of PackedHE in certain scenarios.
Security Assumptions: PackedHE is based on specific cryptographic assumptions, such as RLWE, which may have vulnerabilities or limitations in certain threat models or under advanced attacks.
Scalability: PackedHE approaches may face challenges in scaling to large datasets or complex models, as the computational and communication costs can increase significantly with the size of the data.

In comparison, differential privacy provides strong privacy guarantees by adding noise to the data or query results, but it may introduce trade-offs in terms of accuracy and utility. Secure multi-party computation allows parties to jointly compute a function over their private inputs without revealing the inputs to each other, but it can be computationally intensive and may require complex protocols.

What are the implications of PackVFL's innovations on the broader field of homomorphic encryption and its applications in privacy-preserving computing

The innovations of PackVFL in the field of homomorphic encryption have significant implications for privacy-preserving computing and its applications. Some of the implications include:

Efficiency Improvements: PackVFL's efficient PackedHE techniques and hybrid MatMult method demonstrate the potential for accelerating secure distributed machine learning tasks, enabling faster and more scalable privacy-preserving computations.
Enhanced Security: By leveraging the capabilities of homomorphic encryption, PackVFL enhances the security of distributed machine learning by allowing parties to perform computations on encrypted data without revealing the underlying information.
Cross-Disciplinary Impact: PackVFL's cross-disciplinary approach bridging federated learning and cryptography can inspire further research and innovation in the intersection of machine learning and privacy-preserving technologies.
Real-World Applications: The advancements in PackedHE techniques and efficient MatMult methods can have practical applications in industries where data privacy is a critical concern, such as healthcare, finance, and telecommunications.

Overall, PackVFL's innovations contribute to the advancement of homomorphic encryption and its role in enabling secure and privacy-preserving computing solutions for a wide range of applications.