Digital audio, video, images, and documents are flying through cyberspace to their respective owners. Unfortunately, along the way, individuals may choose to intervene and take this content for themselves. Digital watermarking and steganography technology greatly reduces the instances of this by limiting or eliminating the ability of third parties to decipher the content that he has taken. The many techiniques of digital watermarking (embedding a code) and steganography (hiding information) continue to evolve as applications that necessitate them do the same. The authors of this second edition provide an update on the framework for applying these techniques that they provided researchers and professionals in the first well-received edition. Steganography and steganalysis (the art of detecting hidden information) have been added to a robust treatment of digital watermarking, as many in each field research and deal with the other. New material includes watermarking with side information, QIM, and dirty-paper codes. The revision and inclusion of new material by these influential authors has created a must-own book for anyone in this profession.

*This new edition now contains essential information on steganalysis and steganography
*New concepts and new applications including QIM introduced
*Digital watermark embedding is given a complete update with new processes and applications

Digital Watermarking and Steganography

From the Publisher:
Steganography, a means by which two or more parties may communicate using "invisible" or "subliminal" communication, and watermarking, a means of hiding copyright data in images, are becoming necessary components of commercial multimedia applications that are subject to illegal use. This new book is the first comprehensive survey of steganography and watermarking and their application to modern communications and multimedia. 

Handbook of Information Hiding: Steganography and Watermarking helps you understand steganography, the history of this previously neglected element of cryptography, the hurdles of international law on strong cryptographic techniques, a description of possible applications, and a survey of the methods you can use to hide information in modern media. Included in this discussion is an overview of "steganalysis," methods which can be used to break steganographic communication.

This comprehensive resource also includes an introduction to and survey of watermarking methods, and discusses this method's similarities and differences to steganography. You gain a working knowledge of watermarking's pros and cons, and you learn the legal implications of watermarking and copyright issues on the Internet.

Information Hiding Techniques for Steganography and Digital Watermarking

C4.5: Programs for Machine Learning (書評)

We provide here an overview of the new and rapidly emerging research area of privacy preserving data mining. We also propose a classification hierarchy that sets the basis for analyzing the work which has been performed in this context. A detailed review of the work accomplished in this area is also given, along with the coordinates of each work to the classification hierarchy. A brief evaluation is performed, and some initial conclusions are made.

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State-of-the-art in privacy preserving data mining

Advances in hardware technology have increased the capability to store and record personal data about consumers and individuals, causing concerns that personal data may be used for a variety of intrusive or malicious purposes. Privacy-Preserving Data Mining: Models and Algorithms proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. These techniques generally fall into the following categories: data modification techniques, cryptographic methods and protocols for data sharing, statistical techniques for disclosure and inference control, query auditing methods, randomization and perturbation-based techniques. This edited volume contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions. Privacy-Preserving Data Mining: Models and Algorithms is designed for researchers, professors, and advanced-level students in computer science, and is also suitable for industry practitioners.

Privacy-Preserving Data Mining: Models and Algorithms

: In this paper we present our new paradigm for dealing with the inference problem which arises from downgrading. Our new paradigm has two main parts: the application of decision tree analysis to the inference problem, and the concept of parsimonious downgrading. We also include a new thermodynamically motivated way of dealing with the deduction of inference rules from partial data.

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Parsimonious downgrading and decision trees applied to the inference problem

: We discuss how steganography, in contrast to similar disciplines, requires a new paradigm based upon discontinuities and the absence of noise as a detection deterrent.

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A new paradigm hidden in steganography

At present, "capacity" is the prevailing paradigm for covert channels. With respect to steganography, however, capacity is at best insufficient, and at worst, is incorrect. In this paper, we propose a new paradigm called "capability" which gauges the effectiveness of a steganographic method. It includes payload carrying ability, detectability, and robustness components. We also discuss the use of zero-error capacity for channel analysis and demonstrate that a JPEG compressed image always has the potential to carry hidden information.

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Capacity is the wrong paradigm

One of the central objectives of studying database privacy protection is to protect sensitive information held in a database from being inferred by a generic database user. In this paper, we present a framework to assist in the formal analysis of the database inference problem. The framework is based on an association network which is composed of a similarity measure and a Bayesian network model.

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An Integrated Framework for Database Privacy Protection

The database inference problem is a challenging and important issue in data privacy Database inference for distributed systems is an emerging research area This paper describes a framework and approach to address the inference problem for distributed databases

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LiWu Chang

Papers

Parsimonious downgrading and decision trees applied to the inference problem

A new paradigm hidden in steganography

Capacity is the wrong paradigm

An Integrated Framework for Database Privacy Protection

A Study of Inference Problems in Distributed Databases