The existing cloud outsourcing privacy-preserving k-means clustering schemes have the problem of low efficiency and the problem of returning unreasonable clustering results when the cloud server is untrusted or attacked by hackers. Therefore, a cloud outsourcing verifiable privacy-preserving k-means clustering scheme that can be applied to multi-party privacy-preserving scenarios was proposed. Firstly, an improved clustering initialization method suitable for cloud outsourcing scenarios was proposed to effectively improve the iterative efficiency of the algorithm. Secondly, the multiplicative triple technology was used to design the safe Euclidean distance algorithm, and the garbled circuit technology was used to design the algorithm for safe calculation of the minimum value. Finally, a verification algorithm was proposed, making the users only need one round of communication to verify the clustering results. And after the data outsourcing, the algorithm training was performed on the cloud entirely, which was able to effectively reduce the interactions between users and the cloud. Simulation results show that the accuracy of the proposed scheme is 97% and 93% on the datasets Synthetic and S1 respectively, indicating that the privacy-preserving k-means clustering is similar to the plaintext k-means clustering, and is suitable for medical, social sciences and business fields.

To solve the problem that the existing hierarchical searchable encryption scheme cannot effectively resist quantum attack and cannot flexibly add and delete the level, a scheme of Adaptive Hierarchical Searchable Encryption based on learning with errors (AHSE) was proposed. Firstly, the proposed scheme was made to effectively resist the quantum attack by utilizing the multidimensional characteristic of lattices and based on the Learning With Errors (LWE) problem on lattices. Secondly, the condition key was constructed to divide the users into different levels clearly, making the user only able to search the files at his own level, so as to achieve effective level access control. At the same time, a segmented index structure with good adaptability was designed, whose levels could be added and deleted flexibly, meeting the requirements of access control with different granularities. Moreover, all users in this scheme were able to search by only sharing one segmented index table, which effectively improves the search efficiency. Finally, theoretical analysis shows that the update, deletion and level change of users and files in this scheme is simple and easy to operate, which are suitable for dynamic encrypted database, cloud medical system and other dynamic environments.

Interpolation algorithms are usually used to estimate the channel frequency response value at the data location in Long Term Evolution-Advanced (LTE-A) system. Concerning the problem that traditional Linear Minimum Mean Square Error (LMMSE) algorithm needs to obtain channel statistical properties in advance and it suffers from a high computational complexity due to an inversion matrix operation, an improved LMMSE channel estimation interpolation algorithm was proposed. Firstly, the pilots were interpolated to add virtual pilots, which improved the performance of the algorithm. Secondly, an approximate estimation method of autocorrelation matrix and Signal-to-Noise Ratio (SNR) was given by using the fact that channel energy in the time domain is more concentrated. Finally, a sliding window method was adopted to further simplify the algorithm complexity to complete the LMMSE interpolation in frequency domain. The simulation results show that the overall performance of the proposed algorithm is better than that of linear interpolation method and Discrete Fourier Transform (DFT) interpolation method, and it has similar Bit Error Rate (BER) and Mean Squared Error (MSE) performance with the traditional LMMSE interpolation algorithm. Furthermore, it reduces the complexity by 98.67% compared with traditional LMMSE estimator without degrading the overall BER and MSE performance, so it is suitable for practical engineering applications.

In order to solve the problem that Local Directional Pattern (LDP) adopts the fixed average block method in the face feature extraction process, which cannot reflect the characteristics of different images well, an improved LDP based on interest point location was proposed. The positions of interest points contained rich feature information, and the interest points could be obtained automatically according to particular image. Firstly, the locations of interest points were decided by Speed Up Robust Feature (SURF) algorithm and K-means clustering algorithm. Secondly, 4-direction LDP (4-LDP) coding was calculated by the feature extraction windows established with each interest point as the center. Finally, the Support Vector Machine (SVM) was used to identify the face. The proposed method was evaluated in Yale and FERET databases and compared with the original LDP, 4-LDP and PCA-LDP (feature extraction method combined Principal Component Analysis and LDP). The experimental results show that the proposed method can obviously improve the recognition rate and stability while ensuring the real-time performance of the system.