To provide the privacy of the users who receive some computing services from the cloud, the users must encrypt their documents before outsourcing them to the cloud. Computation on outsourced encrypted data in the cloud rises some complexity to the system especially in the case when an entity would like to find some documents related to a special Keyword. Searchable encryption is a tool for data owners to encrypt their data in a searchable manner. Generally, there exist two kinds of searchable encryption, namely symmetric (secret key) and asymmetric (public key) ones. Most of the public key searchable encryption schemes are vulnerable to the Keyword guessing attack (KGA). In this paper, we propose an attribute-based Keyword search scheme which is proved to be secure against KGA. Also, we formally prove that the proposed scheme is secure against another attack, namely the chosen Keyword attack (CKA) in the random oracle model.

With the growing number of Persian electronic documents and texts, the use of quick and inexpensive methods to access desired texts from the extensive collection of these documents becomes more important. One of the effective techniques to achieve this goal is the extraction of the Keywords which represent the main concept of the text. For this purpose, the frequency of a word in the text can not be a proper indication of its significance and its crucial role. Also, most of the Keyword extraction methods ignore the concept and semantic of the text. On the other hand, the unstructured nature of new texts in news and electronic documents makes it difficult to extract these words. In this paper, an automated, unsupervised method for Keywords extraction in the Persian language that does not have a proper structure is proposed. This method not only takes into account the probability of occurrence of a word and its frequency in the text, but it also understands the concept and semantic of the text by learning word2vec model on the text. In the proposed method, which is a combination of statistical and machine learning methods, after learning word2vec on the text, the words that have the smallest distance with other words are extracted. Then, a statistical equation is proposed to calculate the score of each extracted word using co-occurence and frequency. Finally, words which have the highest scores are selected as the Keywords. The evaluations indicate that the efficiency of the method by the F-measure is 53. 92% which is 11% superior to other methods.

Purpose: Web is the most important medium for access and dissemination of information and therefore, academic libraries have developed websites for communication. Having a website with the highest ranking is a target for each university. As a result, they provide a list of titles, Keywords and descriptions likely to promote their rank. Density Keywords enhance the quality of information retrieval. This research aims to investigate the quantity and quality of Keyword density on Iranian university websites. Methodology: This research was done with a content analysis approach. Research population consisted of fifteen websites related to the central libraries of MSRT universities in Tehran. Data analysis was done using a Keyword density analyzing application software. Findings: the survey indicated that none of the studied libraries had used proper Keywords in their websites. The central library of the University of Tehran scored the highest visibility because it used more relevant Keywords in its website tags. This library also ranked high in Google search results. Conclusion: The research revealed that using Keywords density and proper tag content on library websites can remarkably enhance library website rank and visibility which leads to better retrieval.

Keyword Spotting is a well-known method in document image retrieval which is based on query word image. In this paper, a document image retrieval system based on Keyword spotting and relevance feedback is presented. Relevance feedback as an interactive method is used in this paper to improve the performance of Document Image Retrieval System (DIRS). In the proposed method, we compare several strategies of positive and negative feedbacks which include “Only Positive Feedback”, “Only Negative Feedback” and “Positive and Negative Feedback”. Experiments show that using relevance feedback in DIR outperforms common DIR.