SPEED: Secure, PrivatE, and Efficient Deep learning
This addresses privacy concerns in distributed deep learning for scenarios where data holders distrust third parties and each other, offering a practical solution with broad applicability.
The paper tackles the problem of deep learning under strong privacy constraints by introducing a framework combining collaborative learning, differential privacy, and homomorphic encryption to protect against threats like honest-but-curious servers and colluding data holders, with experiments on image datasets showing it maintains accuracy and privacy.
We introduce a deep learning framework able to deal with strong privacy constraints. Based on collaborative learning, differential privacy and homomorphic encryption, the proposed approach advances state-of-the-art of private deep learning against a wider range of threats, in particular the honest-but-curious server assumption. We address threats from both the aggregation server, the global model and potentially colluding data holders. Building upon distributed differential privacy and a homomorphic argmax operator, our method is specifically designed to maintain low communication loads and efficiency. The proposed method is supported by carefully crafted theoretical results. We provide differential privacy guarantees from the point of view of any entity having access to the final model, including colluding data holders, as a function of the ratio of data holders who kept their noise secret. This makes our method practical to real-life scenarios where data holders do not trust any third party to process their datasets nor the other data holders. Crucially the computational burden of the approach is maintained reasonable, and, to the best of our knowledge, our framework is the first one to be efficient enough to investigate deep learning applications while addressing such a large scope of threats. To assess the practical usability of our framework, experiments have been carried out on image datasets in a classification context. We present numerical results that show that the learning procedure is both accurate and private.