Practitioners and researchers seeking a concise, accessible introduction to secure multi-party computation which quickly enables them to build practical systems or conduct further research will find this essential reading.

This book provides information on theoretically secure multiparty computation (MPC) and secret sharing, and the fascinating relationship between the two concepts.

Now the most used texbook for introductory cryptography courses in both mathematics and computer science, the Third Edition builds upon previous editions by offering several new sections, topics, and exercises. The authors present the core principles of modern cryptography, with emphasis on formal definitions, rigorous proofs of security.

Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.

Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

Pragmatics is the study of human communication: the choices speakers make to express their intended meaning and the kinds of inferences that hearers draw from an utterance in the context of its use. This Handbook surveys pragmatics from different perspectives, presenting the main theories in pragmatic research, incorporating seminal research as well as cutting-edge solutions. It addresses questions of rational and empirical research methods, what counts as an adequate and successful pragmatic theory, and how to go about answering problems raised in pragmatic theory. In the fast-developing field of pragmatics, this Handbook fills the gap in the market for a one-stop resource to the wide scope of today's research and the intricacy of the many theoretical debates. It is an authoritative guide for graduate students and researchers with its focus on the areas and theories that will mark progress in pragmatic research in the future.

In the setting of multiparty computation, sets of two or more parties with p- vate inputs wish to jointly compute some (predetermined) function of their inputs. The computation should be such that the outputs received by the parties are correctly distributed, and furthermore, that the privacy of each party’s input is preserved as much as possible, even in the presence of - versarial behavior. This encompasses any distributed computing task and includes computations as simple as coin-tossing and broadcast, and as c- plex as electronic voting, electronic auctions, electronic cash schemes and anonymous transactions. The feasibility (and infeasibility) of multiparty c- putation has been extensively studied, resulting in a rather comprehensive understanding of what can and cannot be securely computed, and under what assumptions. The theory of cryptography in general, and secure multiparty computation in particular, is rich and elegant. Indeed, the mere fact that it is possible to actually achieve the aforementioned task is both surprising and intriguing.