To solve the problem of complex context and data sparsity, a new algorithm for the tensor decomposition based on context similarity of mobile user was proposed, namely UCS-TF (User-Context-Service Tensor Factorization recommendation). Multi-dimensional context similarity model was established with combining the user context similarity and confidence of similarity. Then, K-neighbor information of the target user was applied to the three-dimensional tensor decomposition, composed by user, context and mobile-service. Therefore, the predicted value of the target user was obtained, and the mobile recommendation was generated. Compared with cosine similarity method, Pearson correlation coefficient method and the improved Cosine1 model, the Mean Absolute Error (MAE) of the proposed UCS-TF algorithm was reduced by 11.1%, 10.1% and 3.2% respectively; and the P@N index of it was also significantly improved, which is better than that of the above methods. In addition, compared with Cosine1 algorithm, CARS2 algorithm and TF algorithm, UCS-TF algorithm had the smallest prediction error on 5%, 20%, 50% and 80% of data density. The experimental results indicate that the proposed UCS-TF algorithm has better performance, and the user context similarity combining with the tensor decomposition model can effectively alleviate the impact of score sparsity.

For the users' privacy security in electronic transactions, an electronic transaction scheme was proposed to protect the users' privacy. The scheme combined the oblivious transfer and ElGamal signature, achieved both traders privacy security in electronic transactions. A user used a serial number to choose digital goods and paid the bank anonymously and correctly. After that, the bank sent a digital signature of the digital goods to the user, then the user interacted with the merchant obliviously through the digital signature that he had paid. The user got the key though the number of exponentiation encryption, the merchant could not distinguish the digital goods ordered. The serial number was concealed and restricted, so the user could not open the message with the unselected serial number, they could and only could get the digital goods they paid. Correctness proof and security analysis shows that the proposed scheme can protect both traders mutual information in electronic transactions and prevent merchant's malicious fraud. The scheme has short signature, small amount of calculation and dynamic changed keys, its security is strong.

The technology of image retrieval on compression domain was researched. Each image in the database was compressed by fractal coding and IFS fractal code was got. Based on the fractal code, the distance of query image and the image in the database was calculated using the distribution character of fractal code. Experiment results show that the algorithm presented is efficient in image retrieval based on IFS code.