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.