Collaborative Homomorphic Computation on Data Encrypted under Multiple Keys
This addresses a bottleneck in secure cloud-based collaborative ML for clients and model owners, though it is incremental.
The paper tackles the inefficiency of homomorphic encryption for multi-key collaborative machine learning by proposing a new approach that eliminates key setup and reduces ciphertext size from (N+1)x2 to 2x2.
Homomorphic encryption (HE) is a promising cryptographic technique for enabling secure collaborative machine learning in the cloud. However, support for homomorphic computation on ciphertexts under multiple keys is inefficient. Current solutions often require key setup before any computation or incur large ciphertext size (at best, grow linearly to the number of involved keys). In this paper, we proposed a new approach that leverages threshold and multi-key HE to support computations on ciphertexts under different keys. Our new approach removes the need for key setup between each client and the set of model owners. At the same time, this approach reduces the number of encrypted models to be offloaded to the cloud evaluator, and the ciphertext size with a dimension reduction from (N+1)x2 to 2x2. We present the details of each step and discuss the complexity and security of our approach.