Web search engines usually keep users’ profiles for multiple purposes, such as result ranking and relevancy, market research, and targeted advertisements. However, user web search history may contain sensitive and private information about the user, such as health condition, personal interests, and affiliations that may infringe users’ privacy since a user’s identity may be exposed and misused by third parties. Numerous techniques are available to address privacy infringement, including Private Information Retrieval protocols that use peer nodes to preserve privacy. Previously, (...) we have proved that PIR protocols are vulnerable to the QuPiD Attack. In this research, we proposed NN-QuPiD Attack, an improved version of QuPiD Attack that uses an Artificial Neural Network based model to associate queries with their original users. The results show that the NN-QuPiD Attack gave 0.512 Recall with the Precision of 0.923, whereas simple QuPiD Attack gave 0.49 Recall with the Precision of 0.934 with the same data. (shrink)

Fake news detection involves predicting the likelihood that a particular news article is intentionally deceptive. Arabic FND started to receive more attention in the last decade, and many detection approaches demonstrated some ability to detect fake news on multiple datasets. However, most existing approaches do not consider recent advances in natural language processing, i.e., the use of neural networks and transformers. This paper presents a comprehensive comparative study of neural network and transformer-based language models used for Arabic FND. We examine (...) the use of neural networks and transformer-based language models for Arabic FND and show their performance compared to each other. We also conduct an extensive analysis of the possible reasons for the difference in performance results obtained by different approaches. The results demonstrate that transformer-based models outperform the neural network-based solutions, which led to an increase in the F1 score from 0.83 to 0.95, and it boosted the accuracy by 16% compared to the best in neural network-based solutions. Finally, we highlight the main gaps in Arabic FND research and suggest future research directions. (shrink)

Mobile app stores provide an extremely rich source of information on app descriptions, characteristics, and usage, and analyzing these data provides insights and a deeper understanding of the nature of apps. However, manual analysis of this vast amount of information on mobile apps is not a simple and straightforward task; it is costly in terms of human effort and time. Computational methods such as topic modeling can provide an efficient and satisfactory approach to mobile app information analysis. Topic modeling is (...) a type of statistical modeling technique for discovering abstract topics that occur in a set of documents. This study explores the relationship between features of Arabic apps and investigates how well the current predefined Google Play app categories represent the type and genre of Arabic mobile apps. Based on the textual app description analysis, we aim to design and develop a sustainable classification system using the Latent Dirichlet Allocation method of topic modeling in order to cover the Arabic apps classification in Google Play app store. Our study supports the hypothesis that the textual app descriptions are effective in suggesting new categories for Arabic mobile apps in Google Play app store. Also, the results indicated that the current classification on Google Play app store is not suitable for our case study “Arabic apps,” as well as it is not sustainable, as it can not cover the new app types including Arabic apps. This study offers an important contribution to Arabic app analysis and design, to improve app search and exploration in several domains such as business, marketing, and technical development. Furthermore, it provides insights for the future of Arabic app research and provides guidance for the development of an Arabic app dashboard that will support users on how to select an app based on their specific needs. (shrink)