Privacy-Preserving Top-k Keyword Similarity Search over Outsourced Cloud Data

In this paper,we study the problem of privacy-preserving top-k keyword similarity search over outsourced cloud data.Taking edit distance as a measure of similarity,we first build up the similarity keyword sets for all the keywords in the data collection.We then calculate the relevance scores of the elements in the similarity keyword sets by the widely used tf-idf theory.Leveraging both the similarity keyword sets and the relevance scores,we present a new secure and efficient treebased index structure for privacy-preserving top-k keyword similarity search.To prevent potential statistical attacks,we also introduce a two-server model to separate the association between the index structure and the data collection in cloud servers.Thorough analysis is given on the validity of search functionality and formal security proofs are presented for the privacy guarantee of our solution.Experimental results on real-world data sets further demonstrate the availability and efficiency of our solution.

A novel denoising method for infrared image based on bilateral filtering and non-local means

This paper presents an image denoising method based on bilateral filtering and non-local means. The non-local region texture or structure of the image has the characteristics of repetition, which can be used to effectively preserve the edge and detail of the image. And compared with classical methods, bilateral filtering method has a better performance in denosing for the reason that the weight includes the geometric closeness factor and the intensity similarity factor. We combine the geometric closeness factor with the weight of non-local means, and construct a new weight. Experimental results show that the modified algorithm can achieve better performance. And it can protect the image detail and structure information better.