ENGINEERING - Cambridge University Press India

Foundations of
Cryptography
Vol. 1: Basic Tools
Oded Goldreich
Weizmann Institute of
Science, Israel
Foundations of
Cryptography
Vol. 2: Basic
Applications
Oded Goldreich
Weizmann Institute of
Science, Israel
of mobile data; 12. Mobility and location
information; 13. Active transactions; 14. Mobile
security; Part IV. Putting the Project Together:
15. The mobile development process;
16. Architecture, design, and technology selection;
17. Mobile application implementation hurdles;
18. Testing; 19. A case study.
ISBN: 9780521696234 878pp ` 595.00
Cryptography is concerned with the
conceptualization, definition and construction of
computing systems that address security
concerns. The design of cryptographic systems
must be based on firm foundations. This book
presents a rigorous and systematic treatment of
the foundational issues: defining cryptographic
tasks and solving new cryptographic problems
using existing tools. It focuses on the basic
mathematical tools: computational difficulty (oneway
functions), pseudorandomness and zeroknowledge
proofs. The emphasis is on the
clarification of fundamental concepts and on
demonstrating the feasibility of solving
cryptographic problems, rather than on describing
ad-hoc approaches. The author assumes basic
familiarity with the design and analysis of
algorithms; some knowledge of complexity theory
and probability is also useful.
Contents: Preface; 1. Introduction;
2. Computational difficulty; 3. Pseudorandom
generators; 4. Zero-knowledge proof systems.
ISBN: 9780521670524 392pp ` 495.00
The second volume contains a rigorous treatment
of three basic applications: Encryption,
Signatures, and General Cryptographic Protocols.
It builds on the previous volume which provided a
treatment of one-way functions,
pseudorandomness and zero-knowledge proofs. It
is suitable for use in a graduate course on
cryptography and as a reference book for experts.
The author assumes basic familiarity with the
design and analysis of algorithms and knowledge
of complexity theory and probability.
Contents: Volume II: Basic Applications:
5. Encryption schemes; 6. Signatures and
message authentication; 7. General cryptographic
protocols; C. Correction and additions to Volume I.
ISBN: 9780521670418 448pp ` 495.00
Information Theory,
Inference and
Learning
Algorithms
David J.C. Mackay
University of Cambridge
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. 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-firstcentury
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.
Contents:1. Introduction to information theory;
2. Probability, entropy, and inference; 3. More
about inference; Part I. Data Compression:
4. The source coding theorem; 5. Symbol codes;
6. Stream codes; 7. Codes for integers; Part II.
Noisy-Channel Coding: 8. Correlated random
variables; 9. Communication over a noisy channel;
10. The noisy-channel coding theorem; 11. Errorcorrecting
codes and real channels; Part III.
Further Topics in Information Theory: 12. Hash
codes: codes for efficient information retrieval;
13. Binary codes; 14. Very good linear codes
exist; 15. Further exercises on information theory;
16. Message passing; 17. Communication over
constrained noiseless channels; 18. Crosswords
and codebreaking;19. Why have sex Information
acquisition and evolution; Part IV. Probabilities and
Inference: 20. An example inference task:
clustering; 21. Exact inference by complete
enumeration; 22. Maximum likelihood and
clustering; 23. Useful probability distributions;
24. Exact marginalization; 25. Exact
marginalization in trellises; 26. Exact
marginalization in graphs; 27. Laplace’s method;
28. Model comparison and Occam’s razor,
29. Morris Carlo methods; 30. Efficient Monte
Carlo methods; 31. Ising models; 32. Exact Monte
Carlo sampling; 33. Variational methods;
34. Independent component analysis and latent
variable modelling; 35. Random inference topics;
36. Decision theory; 37. Bayesian inference and
sampling theory; Part V. Neural Networks:
38. Introduction to neural networks; 39. The single
neuron as a classifier; 40. Capacity of a single
neuron; 41. Learning as inference; 42. Hopfield
networks; 43. Boltzmann machines;
44. Supervised learning in multilayer networks;
45. Gaussian processes; 46. Deconvolution; Part
VI. Sparse Graph Codes: 47. Low-density paritycheck
codes; 48. Convolutional codes and turbo
codes; 49. Repeat-accumulate codes; 50. Digital
fountain codes; Part VII. Appendices: A. Notation;
B. Some physics; C. Some mathematics;
Bibliography; Index.
ISBN: 9780521670517 640pp ` 695.00
45