We propose Falcon, an end-to-end 3-party protocol for efficient private training and inference of large machine learning models. Falcon presents four main advantages – (i) It is highly expressive with support for high capacity networks such as VGG16 (ii) it supports batch normalization which is important for training complex networks such as AlexNet (iii) Falcon guarantees security with abort against malicious adversaries, assuming an honest majority (iv) Lastly, Falcon presents new theoretical insights for protocol design that make it highly efficient and allow it to outperform existing secure deep learning solutions. Compared to prior art for private inference, we are about 8× faster than SecureNN (PETS’19) on average and comparable to ABY3 (CCS’18). We are about 16 − 200× more communication efficient than either of these. For private training, we are about 6× faster than SecureNN, 4.4× faster than ABY3 and about 2−60× more communication efficient. Our experiments in the WAN setting show that over large networks and datasets, compute operations dominate the overall latency of MPC, as opposed to the communication.

No abstract

No abstract

No abstract

This paper aims to enable training and inference of neural networks in a manner that protects the privacy of sensitive data. We propose FALCON -an end-to-end 3-party protocol for fast and secure computation of deep learning algorithms on large networks. FALCON presents three main advantages. It is highly expressive. To the best of our knowledge, it is the first secure framework to support high capacity networks with over a hundred million parameters such as VGG16 as well as the first to support batch normalization, a critical component of deep learning that enables training of complex network architectures such as AlexNet. Next, FALCON guarantees security with abort against malicious adversaries, assuming an honest majority. It ensures that the protocol always completes with correct output for honest participants or aborts when it detects the presence of a malicious adversary. Lastly, FAL-CON presents new theoretical insights for protocol design that make it highly efficient and allow it to outperform existing secure deep learning solutions. Compared to prior art for private inference, we are about 8× faster than SecureNN (PETS '19) on average and comparable to ABY 3 (CCS '18). We are about 16 − 200× more communication efficient than either of these. For private training, we are about 6× faster than SecureNN, 4.4× faster than ABY 3 and about 2 − 60× more communication efficient. This is the first paper to show via experiments in the WAN setting, that for multi-party machine learning computations over large networks and datasets, compute operations dominate the overall latency, as opposed to the communication.