From the Publisher:
A valuable reference for the novice as well as for the expert who needs a wider scope of coverage within the area of cryptography, this book provides easy and rapid access of information and includes more than 200 algorithms and protocols; more than 200 tables and figures; more than 1,000 numbered definitions, facts, examples, notes, and remarks; and over 1,250 significant references, including brief comments on each paper.

Handbook of Applied Cryptography

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

PROBLEM TO BE SOLVED: To solve the problem, wherein it is impossible for an electronic content information provider to provide commercially secure and effective method, for a configurable general-purpose electronic commercial transaction/distribution control system. SOLUTION: In this system, having at least one protected processing environment for safely controlling at least one portion of decoding of digital information, a secure content distribution method comprises a process for encapsulating digital information in one or more digital containers; a process for encrypting at least a portion of digital information; a process for associating at least partially secure control information for managing interactions with encrypted digital information and/or digital container; a process for delivering one or more digital containers to a digital information user; and a process for using a protected processing environment, for safely controlling at least a portion of the decoding of the digital information. COPYRIGHT: (C)2006,JPO&NCIPI

https://apps.dtic.mil/dtic/tr/fulltext/u2/a423264.pdf

Systems and Methods for Secure Transaction Management and Electronic Rights Protection

"Grid" computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and, in some cases, high performance orientation. In this article, the authors define this new field. First, they review the "Grid problem," which is defined as flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources--what is referred to as virtual organizations. In such settings, unique authentication, authorization, resource access, resource discovery, and other challenges are encountered. It is this class of problem that is addressed by Grid technologies. Next, the authors present an extensible and open Grid architecture, in which protocols, services, application programming interfaces, and software development kits are categorized according to their roles in enabling resource sharing. The authors describe requirements that they believe any such mechanisms must satisfy and discuss the importance of defining a compact set of intergrid protocols to enable interoperability among different Grid systems. Finally, the authors discuss how Grid technologies relate to other contemporary technologies, including enterprise integration, application service provider, storage service provider, and peer-to-peer computing. They maintain that Grid concepts and technologies complement and have much to contribute to these other approaches.

The Anatomy of the Grid: Enabling Scalable Virtual Organizations

Admission control is an essential and fundamental security service in mobile ad hoc networks (MANETs). It is needed to securely cope with dynamic membership and topology and to bootstrap other important security primitives (such as key management) and services (such as secure routing) without the assistance of any centralized trusted authority. An ideal admission protocol must involve minimal interaction among the MANET nodes, since connectivity can be unstable. Also, since MANETs are often composed of weak or resource-limited devices, admission control must be efficient in terms of computation and communication. Most previously proposed admission control protocols are prohibitively expensive and require a lot of interaction among MANET nodes in order to securely reach limited consensus regarding admission and cope with potentially powerful adversaries. While the expense may be justified for long-lived group settings, short-lived MANETs can benefit from much less expensive techniques without sacrificing any security. In this paper, we consider short-lived MANETs and present a secure, efficient and a fully non-interactive admission control protocol for such networks. More specifically, our work is focused on novel applications of non-interactive secret sharing techniques based on bi-variate polynomials, but, unlike other results, the associated costs are very low

Efficient node admission for short-lived mobile ad hoc networks

Security and privacy in RFID systems is an important and active research area. A number of challenges arise due to the extremely limited computational, storage and communication abilities of a typical RFID tag. This paper describes a step-by-step construction of a family of simple protocols for inexpensive untraceable identification and authentication of RFID tags. This work is aimed primarily at RFID tags that are capable of performing a small number of inexpensive conventional (as opposed to public key) cryptographic operations. It also represents the first result geared for so-called batch mode of RFID scanning whereby the identification (and/or authentication) of tags is delayed. Proposed protocols involve minimal interaction between a tag and a reader and place very low computational burden on the tag. Notably, they also impose low computational load on back-end servers.

A Family of Dunces: Trivial RFID Identification and Authentication Protocols.

We propose a new biometric based on the human body's response to an electric square pulse signal, called pulse-response. We explore how this biometric can be used to enhance security in the context of two example applications: (1) an additional authentication mechanism in PIN entry systems, and (2) a means of continuous authentication on a secure terminal. The pulse-response biometric is effective because each human body exhibits a unique response to a signal pulse applied at the palm of one hand, and measured at the palm of the other. Using a prototype setup, we show that users can be correctly identified, with high probability, in a matter of seconds. This identification mechanism integrates well with other established methods and offers a reliable additional layer of security, either on a continuous basis or at login time. We build a proof-of-concept prototype and perform experiments to assess the feasibility of pulse-response as a practical biometric. The results are very encouraging, achieving accuracies of 100% over a static data set, and 88% over a data set with samples taken over several weeks.

/pdf/authentication-using-pulse-response-biometrics-w0gvhs99ql.pdf

Authentication using pulse-response biometrics

Constructing practical and provably secure group signature schemes has been a very active research topic in recent years. A group signature can be viewed as a digital signature with certain extra properties. Notably, anyone can verify that a signature is generated by a legitimate group member, while the actual signer can only be identified (and linked) by a designated entity called a group manager. Currently, the most efficient group signature scheme available is due to Camenisch and Lysyanskaya [CL02]. It is obtained by integrating a novel dynamic accumulator with the scheme by Ateniese, et al. [ACJT00].

/pdf/accumulating-composites-and-improved-group-signing-4aelqeehbs.pdf

Accumulating Composites and Improved Group Signing

Constructing practical and provably secure group signature schemes has been a very active research topic in recent years. A group signature can be viewed as a digital signature with certain extra properties. Notably, anyone can verify that a signature is generated by a legitimate group member, while the actual signer can only be identified (and linked) by a designated entity called a group manager. Currently, the most efficient group signature scheme available is due to Camenisch and Lysyanskaya [CL02]. It is obtained by integrating a novel dynamic accumulator with the scheme by Ateniese, et al. [ACJT00]. In this paper, we construct a dynamic accumulator that accumulates composites, as opposed to previous accumulators that accumulated primes. We also present an efficient method for proving knowledge of factorization of a committed value. Based on these (and other) techniques we design a novel provably secure group signature scheme. It operates in the common auxiliary string model and offers two important benefits: 1) the Join process is very efficient: a new member computes only a single exponentiation, and 2) the (unoptimized) cost of generating a group signature is 17 exponentiations which is appreciably less than the state-of-the-art.

Gene Tsudik

Papers

Efficient node admission for short-lived mobile ad hoc networks

A Family of Dunces: Trivial RFID Identification and Authentication Protocols.

Authentication using pulse-response biometrics

Accumulating Composites and Improved Group Signing

Accumulating composites and improved group signing