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

Authentication Using Pulse-Response Biometrics.

Some sensor network settings involve disconnected or unattended operation with periodic visits by a mobile sink. An unattended sensor network operating in a hostile environment can collect data that represents a high-value target for the adversary. Since an unattended sensor can not immediately off-load sensed data to a safe external entity (such as a sink), the adversary can easily mount a focused attack aiming to erase or modify target data. To maximize chances of data survival, sensors must collaboratively attempt to mislead the adversary and hide the location, the origin, and the contents of collected data. In this paper, we focus on applications of well-known security techniques to maximize chances of data survival in unattended sensor networks, where sensed data can not be off-loaded to a sink in real time. Our investigation yields some interesting insights and surprising results. The highlights of our work are: (1) thorough exploration of the data survival challenge, (2) exploration of the design space for possible solutions, (3) construction of several practical and effective techniques, and (4) their evaluation.

Playing hide-and-seek with a focused mobile adversary in unattended wireless sensor networks

Remote attestation (RA) is a popular means of detecting malware in embedded and IoT devices. RA is usually realized as a protocol via which a trusted verifier measures software integrity of an untrusted remote device called prover. All prior RA techniques require on-demand operation. We identify two drawbacks of this approach in the context of unattended devices: First, it fails to detect mobile malware that enters and leaves the prover between successive RA instances. Second, it requires the prover to engage in a potentially expensive computation, which can negatively impact safety-critical or real-time devices. To this end, we introduce the concept of self-measurement whereby a prover periodically (and securely) measures and records its own software state. A verifier then collects and verifies these measurements. We demonstrate a concrete technique called ERASMUS, justify its features, and evaluate its performance. We show that ERASMUS is well-suited for safety-critical applications. We also define a new metric — Quality of Attestation (QoA).

ERASMUS: Efficient remote attestation via self-measurement for unattended settings

Increasing dependence on anytime-anywhere availability of data and the commensurately increasing fear of losing privacy motivate the need for privacy-preserving techniques. One interesting and common problem occurs when two parties need to privately compute an intersection of their respective sets of data. In doing so, one or both parties must obtain the intersection (if one exists), while neither should learn anything about other set. Although prior work has yielded a number of effective and elegant Private Set Intersection (PSI) techniques, the quest for efficiency is still underway. This paper explores some PSI variations and constructs several secure protocols that are appreciably more efficient than the state-of-the-art.

/pdf/practical-private-set-intersection-protocols-with-linear-4ci0jwtd9u.pdf

Practical Private Set Intersection Protocols with Linear Computational and Bandwidth Complexity.

Whole genome sequencing will soon become affordable for many individuals, but thorny privacy and ethical issues could jeopardize its popularity and thwart the large-scale adoption of genomics in healthcare and slow potential medical advances. The Web extra at http://youtu.be/As3J9NYsbbY is an audio recording of Alf Weaver interviewing Bradley Malin and Jacques Fellay about the possibilities and challenges of whole genome sequencing.

/pdf/whole-genome-sequencing-revolutionary-medicine-or-privacy-367pepmc5c.pdf

Gene Tsudik

Papers

Authentication Using Pulse-Response Biometrics.

Playing hide-and-seek with a focused mobile adversary in unattended wireless sensor networks

ERASMUS: Efficient remote attestation via self-measurement for unattended settings

Practical Private Set Intersection Protocols with Linear Computational and Bandwidth Complexity.

Whole Genome Sequencing: Revolutionary Medicine or Privacy Nightmare?