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Two parties wish to collaborate on their datasets. However, before theyreveal their datasets to each other, the parties want to have the guaranteethat the collaboration would be fruitful. We look at this problem from thepoint of view of machine learning, where one party is promised an improvementon its prediction model by incorporating data from the other party. The partieswould only wish to collaborate further if the updated model shows animprovement in accuracy. Before this is ascertained, the two parties would notwant to disclose their models and datasets. In this work, we construct aninteractive protocol for this problem based on the fully homomorphic encryptionscheme over the Torus (TFHE) and label differential privacy, where theunderlying machine learning model is a neural network. Label differentialprivacy is used to ensure that computations are not done entirely in theencrypted domain, which is a significant bottleneck for neural network trainingaccording to the current state-of-the-art FHE implementations. We prove thesecurity of our scheme in the universal composability framework assuminghonest-but-curious parties, but where one party may not have any expertise inlabelling its initial dataset. Experiments show that we can obtain the output,i.e., the accuracy of the updated model, with time many orders of magnitudefaster than a protocol using entirely FHE operations.

Practical, Private Assurance of the Value of Collaboration