To solve the security problems caused by the disclosure of blockchain ledgers， the key lies in the hiding of private information. An attribute-based encryption scheme with multiple authorities was proposed for privacy protection of blockchain data. Compared to single authority， multiple authorities are decentralized and avoid any single point of failure. First， the key component generation algorithm was modified， where each authority used the user identity as a parameter to generate private key components， preventing collusion between nodes to access unauthorized data. Then， identity-based signature technology was modified to establish a connection between user identities and wallet addresses， making the blockchain policeable and the illegal users traceable. Finally， based on the DBDH （Decisional Bilinear Diffie-Hellman） hypothesis， the safety of the proposed scheme was proved in random oracle model. The experimental results show that， compared with the blockchain privacy protection scheme based on the ring signature based on the elliptic curve and the blockchain privacy protection scheme supporting keyword forgetting search， the proposed scheme takes the least amount of time and is more feasible， when generating the same number of blocks.

Safety helmet wearing is a powerful guarantee of workers’ personal safety. Aiming at the collected safety helmet wearing pictures have characteristics of high density， small pixels and difficulty to detect， a small object detection algorithm of YOLOv5 （You Only Look Once version 5） for safety helmet was proposed. Firstly， based on YOLOv5 algorithm， the bounding box regression loss function and confidence prediction loss function were optimized to improve the learning effect of the algorithm on the features of dense small objects in training. Secondly， slicing aided fine-tuning and Slicing Aided Hyper Inference （SAHI） were introduced to make the small object produce a larger pixel area by slicing the pictures input into the network， and the effect of network inference and fine-tuning was improved. In the experiments， a dataset containing dense small objects of safety helmets in the industrial scenes was used for training. The experimental results show that compared with the original YOLOv5 algorithm， the improved algorithm can increase the precision by 0.26 percentage points， the recall by 0.38 percentage points. And the mean Average Precision （mAP） of the proposed algorithm reaches 95.77%， which is improved by 0.46 to 13.27 percentage points compared to several algorithms such as the original YOLOv5 algorithm. The results verify that the introduction of slicing aided fine-tuning and SAHI improves the precision and confidence of small object detection and recognition in the dense scenes， reduces the false detection and missed detection cases， and can satisfy the requirements of safety helmet wearing detection effectively.

In stock market， investors can predict the future stock return by capturing the potential trading patterns in historical data. The key issue for predicting stock return is how to find out the trading patterns accurately. However， it is generally difficult to capture them due to the influence of uncertain factors such as corporate performance， financial policies， and national economic growth. To solve this problem， a Multi-Scale Kernel Adaptive Filtering （MSKAF） method was proposed to capture the multi-scale trading patterns from past market data. In this method， in order to describe the multi-scale features of stocks， Stationary Wavelet Transform （SWT） was employed to obtain data components with different scales. The different trading patterns hidden in stock price fluctuations were contained in these data components. Then， the Kernel Adaptive Filtering （KAF） was used to capture the trading patterns with different scales to predict the future stock return. Experimental results show that compared with those of the prediction model based on Two-Stage KAF （TSKAF）， the Mean Absolute Error （MAE） of the results generated by the proposed method is reduced by 10%， and the Sharpe Ratio （SR） of the results generated by the proposed method is increased by 8.79%， verifying that the proposed method achieves better stock return prediction performance.

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）.

To solve the problem of discontinuity when blending two surfaces with coplanar perpendicular axis, this paper discussed how to improve the equations about the blending surface so as to obtain the smooth and continuous blending surface. At first, this paper analyzed the reason of the uncontinuousness in the blending surface and pointed out that the items in one variable were removed when other variables equaled to some specified values. In this case, the blending equation was independent to this variable in these values and this indicated that the belending surface was disconnected. Then, a method which guarantees the blending surface countinuous was presented on the basis of above discussion. Besides this, this paper discussed how to smoothen it once the continuous blending surface was computed out. As for the G0 blending surface, regarding the polynomial of auxiliary surface as a factor, this factor was mulitiplied to a function f′ with degree one and the result was added to the primary surface fi. The smoothness of blending surface can be implemented by changing the coefficients in f. For the Gn blending surface, a compensated polynomial with degree at most 2 was added to the proposed primary blending equation directly when computing blending surface. This method smoothens the blending surface but does not increase the degree of G0 blending surface.

With the rapid development of digital product, the copyright protection of PDF documents has become a crucial research topic in the information security field. This paper analyzed the structure of PDF documents and the relative digital watermarking algorithms. In order to solve the problem that some large capacity text watermarking algorithms increase the size of the document, the authors proposed a digital watermarking algorithm based on the structure of PDF document. Considering the characteristics that end-of-line marker cannot be shown in the document, this algorithm managed to embed watermark through replacing end-of-line markers of the cross-reference table with a fixed format. The experimental results show that, the proposed algorithm can not only reach the standard of watermark capacity required by digital copyright protection, but also has good concealment and can resist statistics attack as well.

In order to provide a logical basis and mathematical model for directly processing natural language, the Lukasiewicz implication algebra based on the graded linguistic values chain and the linguistic truth-valued concept lattice were established. The natural language used to depict certain values in practical problems was analyzed and equivalently expressed as the graded linguistic values set, on which the definitions of partial order relations and binary operators were given. The bijective relation was established between the graded linguistic values chain and the natural language set, and the specific linguistic truth-valued concept lattice was constructed based on the linguistic truth-valued lattice implication algebra. And then, the linguistic truth-valued concept lattice was applied into the analytical system with natural language for vehicle transport safety performance, which verified the feasibility of the model to directly deal with the natural language and the readability of the structure graph.

In order to guide commercial decisions for cross marketing, a new classification algorithm named CHC based on frequent closed itemsets and imprecise reasoning was proposed. The H-C algorithm for mining frequent closed itemsets based on hyperlinked data structure, H-Struct, was improved. The header table of H-Struct was adjusted by inserting the class label to prune the search space; the local relative support and maximum support were used to exclude meaningless patterns; the maximum length of patterns mined was applied to improve the usability of rules. The reasoning algorithm of EMYCIN was extended to handle the rules whose right is negative. The algorithm improved above traditional classification algorithm's limitations in deriving only class label. Furthermore, this algorithm obtained a value referring to the confidence of the classification result to facilitate and simplify the process of evaluating multiple cross marketing plans. The experimental results show that the enhanced algorithm is efficient in run time and classification precision.