This book introduces the fundamental concepts of homomorphic encryption. From these foundations, applications are developed in the fields of private information retrieval, private searching on streaming data, privacy-preserving data mining, electronic voting and cloud computing. The content is presented in an instructional and practical style, with concrete examples to enhance the reader's understanding. This volume achieves a balance between the theoretical and the practical components of modern information security. Readers will learn key principles of homomorphic encryption as well as their application in solving real world problems.

This book deals with Private Information Retrieval (PIR), a technique allowing a user to retrieve an element from a server in possession of a database without revealing to the server which element is retrieved. PIR has been widely applied to protect the privacy of the user in querying a service provider on the Internet. For example, by PIR, one can query a location-based service provider about the nearest car park without revealing his location to the server. The first PIR approach was introduced by Chor, Goldreich, Kushilevitz and Sudan in 1995 in a multi-server setting, where the user retrieves information from multiple database servers, each of which has a copy of the same database. To ensure user privacy in the multi-server setting, the servers must be trusted not to collude. In 1997, Kushilevitz and Ostrovsky constructed the first single-database PIR. Since then, many efficient PIR solutions have been discovered. Beginning with a thorough survey of single-database PIR techniques, this text focuses on the latest technologies and applications in the field of PIR. The main categories are illustrated with recently proposed PIR-based solutions by the authors. Because of the latest treatment of the topic, this text will be highly beneficial to researchers and industry professionals in information security and privacy.

This book is the first monograph in the field of uniqueness theory of meromorphic functions dealing with conditions under which there is the unique function satisfying given hypotheses. Developed by R. Nevanlinna, a Finnish mathematician, early in the 1920's, research in the field has developed rapidly over the past three decades with a great deal of fruitful results. This book systematically summarizes the most important results in the field, including many of the authors' own previously unpublished results. In addition, useful skills and simple proofs are introduced. This book is suitable for higher level and graduate students who have a basic grounding in complex analysis, but will also appeal to researchers in mathematics.

This book is the first monograph in the field of uniqueness theory of meromorphic functions dealing with conditions under which there is the unique function satisfying given hypotheses. Developed by R. Nevanlinna, a Finnish mathematician, early in the 1920's, research in the field has developed rapidly over the past three decades with a great deal of fruitful results. This book systematically summarizes the most important results in the field, including many of the authors' own previously unpublished results. In addition, useful skills and simple proofs are introduced. This book is suitable for higher level and graduate students who have a basic grounding in complex analysis, but will also appeal to researchers in mathematics.

This book constitutes the refereed post-conference proceedings of the 17th EAI International Conference on Quality, Reliability, Security and Robustness in Heterogeneous Networks, QShine 2021, held in November 2020. Due to COVID-19 pandemic the conference was held virtually. The 20 revised full papers were carefully reviewed and selected from 43 submissions. The papers are organized thematically in tracks Machine Learning in Distributed Networks; 5G Networks and Security; IoT Security and Lightweight Cryptography; Network Security; and Privacy-preserving Emerging Networked Applications.

With the massive increase of data and traffic on the Internet within the 5G, IoT and smart cities frameworks, current network classification and analysis techniques are falling short. Novel approaches using machine learning algorithms are needed to cope with and manage real-world network traffic, including supervised, semi-supervised, and unsupervised classification techniques. Accurate and effective classification of network traffic will lead to better quality of service and more secure and manageable networks. This authored book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. The authors explore novel methods for enhancing network statistics at the transport layer, helping to identify optimal feature selection through a global optimization approach and providing automatic labelling for raw traffic through a SemTra framework to maintain provable privacy on information disclosure properties.