CryptoNN: Training Neural Networks over Encrypted Data

Xu, Ran; Joshi, James; Li, Chao

doi:10.1109/icdcs.2019.00121

Cited by 70 publications

(72 citation statements)

References 40 publications

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“…This type of methods designs speciic protocols for privacy preserving neural networks using cryptographic techniques such as secure Multi-Party Computation (MPC) techniques and Homomorphic Encryption (HE). For example, some existing researches built privacy preserving neural network using HE [17,25,48,50]. To use these methods, the participants irst need to encrypt their private data and then outsource these encrypted data to a server who trains neural networks using HE techniques.…”

Section: Cryptographic Methodsmentioning

confidence: 99%

Toward Scalable and Privacy-preserving Deep Neural Network via Algorithmic-Cryptographic Co-design

Chen

Zhou²,

Zheng

et al. 2022

ACM Trans. Intell. Syst. Technol.

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Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has become a serious problem currently. Existing works build privacy preserving DNN models from either algorithmic perspective or cryptographic perspective. The former mainly splits the DNN computation graph between data holders or between data holders and server, which demonstrates good scalability but suffers from accuracy loss and potential privacy risks. In contrast, the latter leverages time-consuming cryptographic techniques, which has strong privacy guarantee but poor scalability. In this paper, we propose SPNN — a S calable and P rivacy-preserving deep N eural N etwork learning framework, from algorithmic-cryptographic co-perspective. From algorithmic perspective, we split the computation graph of DNN models into two parts, i.e., the private data related computations that are performed by data holders and the rest heavy computations that are delegated to a semi-honest server with high computation ability. From cryptographic perspective, we propose using two types of cryptographic techniques, i.e., secret sharing and homomorphic encryption, for the isolated data holders to conduct private data related computations privately and cooperatively. Furthermore, we implement SPNN in a decentralized setting and introduce user-friendly APIs. Experimental results conducted on real-world datasets demonstrate the superiority of our proposed SPNN.

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Section: Cryptographic Methodsmentioning

confidence: 99%

Toward Scalable and Privacy-preserving Deep Neural Network via Algorithmic-Cryptographic Co-design

Chen

Zhou²,

Zheng

et al. 2022

ACM Trans. Intell. Syst. Technol.

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“…In 2019, CryptoNN [13] was proposed as a framework that supports both training and inference phases over encrypted data. This was possible due to the secure matrix computation based on functional encryption.…”

Section: Cryptonnmentioning

confidence: 99%

Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview

Saj

Gooya²,

Alfalou³

et al. 2021

Electronics

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Privacy-preserving deep neural networks have become essential and have attracted the attention of many researchers due to the need to maintain the privacy and the confidentiality of personal and sensitive data. The importance of privacy-preserving networks has increased with the widespread use of neural networks as a service in unsecured cloud environments. Different methods have been proposed and developed to solve the privacy-preserving problem using deep neural networks on encrypted data. In this article, we reviewed some of the most relevant and well-known computational and perceptual image encryption methods. These methods as well as their results have been presented, compared, and the conditions of their use, the durability and robustness of some of them against attacks, have been discussed. Some of the mentioned methods have demonstrated an ability to hide information and make it difficult for adversaries to retrieve it while maintaining high classification accuracy. Based on the obtained results, it was suggested to develop and use some of the cited privacy-preserving methods in applications other than classification.

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“…CryptoNN [79] is a privacy-preserving method that utilizes functional encryption for arithmetic computation over encrypted data. The FE scheme protects the data in the shape of a feature vector inside matrices.…”

Section: ) Year 2019mentioning

confidence: 99%

Privacy-Preserving Deep Learning on Machine Learning as a Service—a Comprehensive Survey

et al. 2020

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The exponential growth of big data and deep learning has increased the data exchange traffic in society. Machine Learning as a Service, (MLaaS) which leverages deep learning techniques for predictive analytics to enhance decision-making, has become a hot commodity. However, the adoption of MLaaS introduces data privacy challenges for data owners and security challenges for deep learning model owners. Data owners are concerned about the safety and privacy of their data on MLaaS platforms, while MLaaS platform owners worry that their models could be stolen by adversaries who pose as clients. Consequently, Privacy-Preserving Deep Learning (PPDL) arises as a possible solution to this problem. Recently, several papers about PPDL for MLaaS have been published. However, to the best of our knowledge, no previous paper has summarized the existing literature on PPDL and its specific applicability to the MLaaS environment. In this paper, we present a comprehensive survey of privacypreserving techniques, starting from classical privacy-preserving techniques to well-known deep learning techniques. Additionally, we present a detailed description of PPDL and address the issue of using PPDL for MLaaS. Furthermore, we undertake detailed comparisons between state-of-the-art PPDL methods. Subsequently, we classify an adversarial model on PPDL by highlighting possible PPDL attacks and their potential solutions. Ultimately, our paper serves as a single point of reference for detailed knowledge on PPDL and its applicability to MLaaS environments for both new and experienced researchers.

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CryptoNN: Training Neural Networks over Encrypted Data

Cited by 70 publications

References 40 publications

Toward Scalable and Privacy-preserving Deep Neural Network via Algorithmic-Cryptographic Co-design

Toward Scalable and Privacy-preserving Deep Neural Network via Algorithmic-Cryptographic Co-design

Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview

Privacy-Preserving Deep Learning on Machine Learning as a Service—a Comprehensive Survey

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