To meet the needs of data sharing in the context of digitalization currently， and take into account the necessity of protecting private data security at the same time， a blockchain smart contract private data authorization method based on TrustZone was proposed. The blockchain system is able to realize data sharing in different application scenarios and meet regulatory requirements， and a secure isolation environment was provided by TrustZone Trusted Execution Environment （TEE） technology for private computing. In the integrated system， the uploading of private data was completed by the regulatory agency， the plaintext information of the private data was obtained by other business nodes only after obtaining the authorization of the user. In this way， the privacy and security of the user were able to be protected. Aiming at the problem of limited memory space in the TrustZone architecture during technology fusion， a privacy set intersection algorithm for small memory conditions was proposed. In the proposed algorithm， the intersection operation for large-scale datasets was completed on the basis of the ??grouping computing idea. The proposed algorithm was tested with datasets of different orders of magnitude. The results show that the time and space consumption of the proposed algorithm fluctuates in a very small range and is relatively stable. The variances are 1.0 s² and 0.01 MB² respectively. When the order of magnitudes of the dataset is increased， the time consumption is predictable. Furthermore， using a pre-sorted dataset can greatly improve the algorithm performance.

Aiming at the copyright protection problem of 3D medical images and the simultaneous expansion problem of watermark storage capacity caused by the increase of the number of images to be protected， a robust zero-watermarking algorithm based on ray-casting sampling and polar complex exponential moment was proposed. Firstly， a sampling algorithm based on ray-casting was proposed to sample the features of 3D medical images composed of multiple sequences of 2D medical images and describe these features in 2D image space. Secondly， a robust zero-watermarking algorithm for 3D medical images was proposed. In the algorithm， three 2D feature images of coronal， sagittal planes and cross section of the 3D medical image were obtained by ray-casting sampling， and the three 2D feature images were transformed by polar complex exponential to obtain the quaternion orthogonal moment. Finally， the zero-watermarking information was constructed by using the quadratic orthogonal moment and Logistic chaotic encryption. Simulation results show that the proposed algorithm can maintain the bit correctness rate of zero-watermarking extraction above 0.920 0 under various common image processing attacks and geometric attacks； the watermark storage capacity of the proposed algorithm can be improved with the increase of the volume of 3D medical image data， and the storage capacity of the proposed algorithm has been improved by 93.75% at least compared to the other 2D medical image zero-watermarking algorithms for comparison.

Aiming at the problems of large area and large consumption of fresh randomness in Threshold Implementation （TI） of SM4， an improved threshold implementation scheme of SM4 was proposed. In the case of satisfying the threshold implementation theory， the operation of S-box nonlinear inversion was shared with no fresh randomness， and a domain-oriented multiplication mask scheme was introduced to reduce the fresh randomness consumption of S-box to 12 bits. Based on the idea of the pipeline， a new SM4 serial architecture with 8-bit data width was designed. The threshold implementation of S-box was reused， and the linear function of SM4 was optimized to make the area of threshold implementation of SM4 more compact， only 6 513 GE. In comparison with the TI scheme of SM4 with 128-bit data width， the area of the proposed scheme is reduced by more than 63.7%， and there is a better trade-off between speed and area. The side-channel experimental results show that the proposed scheme has the capability of anti-first-order Differential Power Analysis （DPA）.

Aiming at the problem that existing watermarking algorithms based on deep learning cannot effectively protect the copyright of high-dimensional medical images， a medical image watermarking algorithm based on multiscale knowledge learning was proposed for the copyright protection of diffusion-weighted images. First， a watermark embedding network based on multiscale knowledge learning was proposed to embed watermarks， and the semantic， texture， edge and frequency domain information of the diffusion-weighted image was extracted by a fine-tuned pre-training network as multiscale knowledge features. Then， the multiscale knowledge features were combined to reconstruct the diffusion-weighted image， and a watermark was embedded during the process redundantly to obtain a watermarked diffusion-weighted image highly similar to the original one visually. Finally， a watermark extraction network based on pyramid feature learning was proposed to improve the robustness of the algorithm by learning the distribution correlation of watermarking signals from different scales of context in the watermarked diffusion-weighted image. Experimental results show that the average Peak Signal-to-Noise Ratio （PSNR） of the reconstructed watermarked images by the proposed algorithm reaches 57.82 dB. Since diffusion-weighted images need to meet certain diffusivity features when converting to diffusion tensor images， the proposed algorithm only has 8 pixel points with the deflection angle of the principal axis direction greater than 5°， and none of these 8 pixel points is in the region of interest of the image. Besides， both of the Fraction Anisotropy （FA） and the Mean Diffusivity （MD） of the image generated by the proposed algorithm are close to 0， which fully meets the requirements of clinical diagnosis. At the same time， facing common noise attacks such as those with cropping strength less than 0.7 and rotation angle less than 15， the proposed algorithm achieves more than 95% watermarking accuracy and can effectively protect the copyright information of diffusion-weighted images.

This paper represented the information of each vertex in ontology graph as a vector. According to its structure of ontology graph, the vertices were divided into k parts. It chose vertices from each part, and chose the ranking loss function. It used k-partite ranking learning algorithm to get the optimization ranking function, mapped each vertex of ontology structure graph into a real number, and then calculated the relative similarities of concepts by comparing the difference between real numbers. The experimental results show that the method for calculating the relative similarity between the concepts of ontology is effective.

A binary code of the compression algorithm is presented. The black and white run-lengths in the binary code stream are compressed halved, which could also be progressed by selecting different initial length according to the different length distribution. Numerical experiments show that the algorithm has a better compression efficiency compare with that of the traditional methods of run-length code, and has good application value.

In the speaker verification field, the distributions of the target and imposter scores are bimodal, and the output score distributions of different target speaker models are different too, so it is difficult to estimate a common threshold. The most frequently used score normalization techniques: T-Norm and Z-Norm were analyzed. A new technique D-Norm was introduced based on the KL distance. Then a new method ZD-Norm was proposed based on Z-Norm and D-Norm. The experiments show that the ZD-Norm method can improve the performance efficiently and make the estimation of threshold easier.