To solve the problem of query restriction in supply-chain financial blockchain system, this paper proposes a privacy budget management and noise reusing method in multichain blockchain environment based on Hyperledger multichannel technology and community clustering algorithm. A historical record book is established to manage the privacy budget according to historical query types, and a differential privacy protection algorithm based on noise reusing is used to generate and reusing noise. In the experiments, we tested the privacy budget loss, data utility and system performance based on the business data-set from chemical supply chain. The experimental results show that the blockchain system with multichain structure based on community clustering algorithm reduces the system data storage space and decreases request processing time effectively. The privacy budget loss of the proposed method is about 1/3 of that of the pure Gaussian mechanism method. After 100 queries, the total amount of noise is about 8% less than that of pure Gaussian mechanism. Besides that, the noise reusing algorithm reduces the loss of the privacy budget and breaks through the query limitation caused by privacy budget wasting.

Terahertz (THz) wave is an electromagnetic wave with a frequency between far infrared ray and millimeter wave, which is widely used in hazardous material detection for its waveband fingerprint spectroscopy. THz time-domain spectroscopy technology based on deep learning can be used for nondestructive detection of various hazardous materials by recognizing the fingerprint spectrum of substances. However, due to the high cost of collecting spectral data, training samples are not easy to obtain and scarce for classification models, which leads to poor training effectiveness and low accuracy of classification. To address this problem, a fully connected layer-based auxiliary classifier generative adversarial network (FC-ACGAN) data augmentation method is proposed in this paper, we realized the generator and discriminator with fully connected layers to fit original data distribution better and generate data with higher quality. First, THz time-domain spectral data from seven flammable liquids were augmented using Mixup and FC-ACGAN, and then we fed the generated data set and expanded data set into Residual Network (ResNet), convolutional neural network, fully convolutional network, and multilayer perceptron for training. It is demonstrated that our method can solve the overfitting of models because of insufficient data. Compared with direct training on original data set, the accuracy of models using augmented data set improved by 5.1325% on average, which is 3.15% higher than that using Mixup. Furthermore, we experimented on expanded data set with ResNet long short-term memory for classification, the final accuracy reaches 99.42% on average, which is 1.09% higher than that using the original data set.