Efficient Cloud-based Secret Shuffling via Homomorphic Encryption
This addresses privacy concerns for data providers in cloud-based multi-party computation scenarios, though it is an incremental improvement in efficiency.
The paper tackles the problem of secretly shuffling confidential data from multiple sources in cloud-based multi-party computation to protect data provider privacy, resulting in a protocol with constant round complexity and linear computational complexity.
When working with joint collections of confidential data from multiple sources, e.g., in cloud-based multi-party computation scenarios, the ownership relation between data providers and their inputs itself is confidential information. Protecting data providers' privacy desires a function for secretly shuffling the data collection. We present the first efficient secure multi-party computation protocol for secret shuffling in scenarios with a central server. Based on a novel approach to random index distribution, our solution enables the randomization of the order of a sequence of encrypted data such that no observer can map between elements of the original sequence and the shuffled sequence with probability better than guessing. It allows for shuffling data encrypted under an additively homomorphic cryptosystem with constant round complexity and linear computational complexity. Being a general-purpose protocol, it is of relevance for a variety of practical use cases.