The sensor data of sensor node is easy to be influenced by the external environment, which makes the incomplete data exist widely in the wireless sensor network and the sensor data face the serious privacy threat. Aiming at the problem of privacy leakage during the query process of incomplete data in two-tiered sensor networks, a Privacy-Preserving Incomplete data Skyline query protocol in two-tiered sensor network (PPIS) based on replacement algorithm and bucket technology was proposed. In order to realize the Skyline query for incomplete data, the value of the missing attribute was replaced to the upper bound of data field and then the incomplete data was mapped into the buckets. In order to preserve the privacy of data, the range of the bucket was transformed into a prefix encoding and then the prefix encoding was loaded into Bloom filters. Thus, the query processing could be executed by the storage node without clear text of the sensor data and real range of the bucket. In order to preserve the integrity of query results, Merkle hash tree was used to construct the integrity verification code for implementing the integrity verification of query results. Theoretical analysis and simulation experiment of real dataset has confirmed the privacy and efficiency of PPIS. Compared with existing privacy-preserving Skyline query protocols-SMQ (Secure Multidimensional Query) and SSQ (Secure Skyline Query), the proposed PPIS can save the communication cost by more than 70%.

As a kind of common method for data preprocessing, feature selection can not only improve the classification performance, but also increase the interpretability of the classification results. In sparse-learning-based feature selection methods, some useful discriminative information is ignored, and it may affect the final classification performance. To address this problem, a new discriminative feature selection method called Discriminative Least Absolute Shrinkage and Selection Operator (D-LASSO) was proposed to choose the most discriminative features. In detail, firstly, the proposed D-LASSO method contained a L ₁-norm regularization item, which was used to produce sparse solution. Secondly, in order to induce the most discriminative features, a new discriminative regularization term was introduced to embed the geometric distribution information of samples with the same class label and samples with different class labels. Finally, the comparison experimental results obtained from a series of Benchmark datasets show that, the proposed D-LASSO method can not only improve the classification accuracy, but also be robust against parameters.

In order to protect user's identity privacy and data security for access control in wireless sensor networks, a privacy-preserving access control protocol in multi-user wireless sensor networks was proposed. The protocol employed attribute-based encryption algorithms and distributed access control mode, using the attribute certificates, digital signatures and threshold mechanism to achieve the pay access, fine-grained access control and anonymous access. And it also ensured the confidentiality of data transmission and data integrity of query command. Analysis and protocol comparison shows that the proposed protocol has several advantages over the current access control methods: lower cost in computation, communication and storage, better scalability and better adaptation to access control requirement of payment wireless sensor network.