Industrial Scale Privacy Preserving Deep Neural Network

Abstract

Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction. Meanwhile, data isolation has become a serious problem currently, i.e., different parties cannot share data with each other. To solve this issue, most research leverages cryptographic techniques to train secure DNN models for multi-parties without compromising their private data. Although such methods have strong security guarantee, they are difficult to scale to deep networks and large datasets due to its high communication and computation complexities. To solve the scalability of the existing secure Deep Neural Network (DNN) in data isolation scenarios, in this paper, we propose an industrial scale privacy preserving neural network learning paradigm, which is secure against semi-honest adversaries. Our main idea is to split the computation graph of DNN into two parts, i.e., the computations related to private data are performed by each party using cryptographic techniques, and the rest computations are done by a neutral server with high computation ability. We also present a defender mechanism for further privacy protection. We conduct experiments on real-world fraud detection dataset and financial distress prediction dataset, the encouraging results demonstrate the practicalness of our proposal.

References in corpus (7)

• Adam: A Method for Stochastic Optimization
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
• Split learning for health: Distributed deep learning without sharing raw patient data
• Secure Social Recommendation based on Secret Sharing
• Privacy Preserving PCA for Multiparty Modeling
• Practical Privacy Preserving POI Recommendation

Cited by in corpus (8)

• Large-Scale Secure XGB for Vertical Federated Learning
• Two-Server Verifiable Homomorphic Secret Sharing for High-Degree Polynomials
• When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
• Shared MF: A privacy-preserving recommendation system
• PPCA: Privacy-preserving Principal Component Analysis Using Secure Multiparty Computation(MPC)
• ASFGNN: Automated Separated-Federated Graph Neural Network
• Secret Sharing based Secure Regressions with Applications
• Practical Two-party Privacy-preserving Neural Network Based on Secret Sharing