Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information of users may be disclosed during data collection, or during training, or even after releasing the trained learning model. Differential privacy (DP) is one of the main approaches proven to ensure strong privacy protection in data analysis. DP protects the users' privacy by adding noise to the original dataset or the learning parameters. Thus, an attacker could not retrieve the sensitive information of an individual involved in the training dataset. In this survey paper, we analyze and present the main ideas based on DP to guarantee users' privacy in deep and federated learning. In addition, we illustrate all types of probability distributions that satisfy the DP mechanism, with their properties and use cases. Furthermore, we bridge the gap in the literature by providing a comprehensive overview of the different variants of DP, highlighting their advantages and limitations. Our study reveals the gap between theory and application, accuracy, and robustness of DP. Finally, we provide several open problems and future research directions.
Delay tolerant networking (DTN) is able to provide communication services in areas where there is no guarantee that a fully connected path between source and destination exists at any time, and seek to address the technical routing issues in wireless networks. Traditional routing protocols are unable to deliver messages between hosts in such conditions. PRoPHET protocol is among the most widely used routing protocols for such networks. The choice of message forwarding strategies can have a major impact on system performance. In this paper, we propose a controlled forwarding strategy for probabilistic routing protocols that can optimize different performance metrics. Based on some assumptions, we have set new rules for message transmission to neighboring nodes. We show that PRoPHET protocol along with our new forwarding strategy can result in much performance improvement in terms of delivered and relayed messages. Learning automata is used to give the optimal threshold value.
Due to the rapid growth of electronic documents, e.g., tweets, blogs, Facebook posts, snaps in different languages that use the same writing script, language categorization, and processing have great importance. For instance, to identify COVID-19 positive patients or people’s emotions on COVID-19 pandemic from tweets written in 35 different languages faster and accurate, language categorization and processing of tweets is significantly essential. Among many language categorization and processing techniques, character and word
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-gram based techniques are very popular and simple but very efficient for categorizing and processing both short and large documents. One of the fundamental problems of language processing is the efficient use of memory space in implementing a technique so that a vast collection of documents can be easily categorized and processed. In this paper, we introduce a framework that categorizes the language of tweets using
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-gram based language categorization technique and further processes the tweets using the machine-learning approach, Linear Support Vector Machine (LSVM), that may be able to identify COVID-19 positive patients. We evaluate and compare the performance of the proposed framework in terms of language categorization accuracy, precession, recall, and F-measure over n-gram length. The proposed framework is scalable as many other applications that involve extracting features and classifying languages collected from social media, and different types of networks may use this framework. This proposed framework, also being a part of health monitoring and improvement, tends to achieve the goal of having a sustainable society.
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