Is It Possible to Hide My Key into Deep Neural Network The use of cryptographic functions has become vital for various devices, such as PCs, smart phones, drones, and smart appliances; however, the secure storage of cryptographic keys (or passwords) is a major issue. One way to securely store such a key is to register the key using secret data such as biometric data and then regenerate the key whenever it is needed. In this paper, we present a novel methodology for hiding cryptographic keys inside a deep neural network (DNN), and is termed as the DNNbased key hiding scheme. In this method, DNNs are constructed and trained with noisy data to hide the key within the network. To prove that our methodology works in practice, we propose an example of the DNNbased key hiding scheme and prove its correctness. For its robustness, we propose two basic security analysis tools to be able to check the examplexe2x80x99s security. To the best of our knowledge, this is the first attempt of its kind.

Optimization Strategies in Quantized Neural Networks: A Review Deep neural networks(DNNs) have achieved great success in many realworld applications, but they also had some drawbacks such as considerable storage requirement, large computational power consumption and delay for training and inference, making it impracticable to deploy stateoftheart models into embedded systems and portable devices. Thus, the demand of compressing DNNs has been taken into consideration. In this paper, we focus on quantized neural networks, which is one scheme of compressing DNNs. At first we introduce some baseline works in quantized neural networks and then give a review on optimization ways used in quantizing neural networks. In our perspective, these methods fall into two categories: minimizing quantization error and minimizing loss function. Specialized introduction for each category follows after baseline works. We also make some comments to each category and some methods. Finally, we discuss on some possible directions of this area and make a conclusion.