However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks ...
In this paper, we propose a Shift-accumulation-based LHE-enabled deep neural network (SHE) for fast and accurate inferences on encrypted data. We use the binary-operation-friendly Leveled Fast Homomorphic Encryption over Torus (LTFHE) encryption scheme to implement ReLU activations and max pooling...
(2012). Ml confidential: Machine learning on encrypted data. In: International Conference on Information Security and Cryptology, pp. 1–21. Springer Hesamifard, E., Takabi, H., & Ghasemi, M. (2017). Cryptodl: Deep neural networks over encrypted data. arXiv:1711.05189 Ishai, Y., Kilian...
《Machine learning classification over encrypted data》 介绍:出自MIT,研究加密数据高效分类问题. 《purine2》 介绍:新加坡LV实验室的神经网络并行框架Purine: A bi-graph based deep learning framework,支持构建各种并行的架构,在多机多卡,同步更新参数的情况下基本达到线性加速。12块Titan 20小时可以完成Googlenet...
2016). To get over the difficulty that deep networks are not easily optimized, the ResNet-18 network uses a residual structure. Each residual block is a multilayer neural network consisting of a convolutional layer, a batch normalization layer, and an activation layer. The new technique ...
For privacy protection, the research community has resorted to advanced cryptographic primitives to support DNN inference over encrypted data. Nevertheless, these attempts are limited by the real-time performance due to the heavy IoT computational overhead brought by cryptographic primitives. In this ...
In this work we do not consider the problem of privacy-preserving data- mining, intended as training a neural network over encrypted data, which can be addressed, e.g., with the approach of [AS00]. Instead, we assume that the neural network is trained with data in the clear and we ...
《Machine learning classification over encrypted data》 介绍:出自MIT,研究加密数据高效分类问题. 《purine2》 介绍:新加坡LV实验室的神经网络并行框架Purine: A bi-graph based deep learning framework,支持构建各种并行的架构,在多机多卡,同步更新参数的情况下基本达到线性加速。12块Titan 20小时可以完成Googlenet的训...
Rory Quann is Head of International Sales at SS8 Networks and brings with him over 10 years of experience in the Lawful Interception and Data Analysis industry. Prior to joining SS8 in 2013, Rory worked for BAE System Applied Intelligence where he was focused on large scale Government deployments...
HE facilitates computations on encrypted data without decryption, thereby preserving data privacy. This presents a significant advantage over other privacy-preserving techniques such as differential privacy, secure multi-party computation (SMPC), and federated learning in the context of credit risk ...