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

Proceedings of the 13th international conference on Information security

With the growing popularity of the Internet-of-Things (IoT), massive numbers of specialized devices are deployed worldwide, in many everyday settings, including homes, offices, vehicles, public spaces, and factories. Such devices usually perform sensing and/or actuation. Many of them handle sensitive and personal data. If left unprotected, ambient sensing (e.g., of temperature, motion, audio, or video) can leak very private information. At the same time, some IoT devices use low-end computing platforms with few (or no) security features.There are many well-known techniques to secure sensed data, e.g., by authenticating communication end-points, encrypting data before transmission, and obfuscating traffic patterns. Such techniques protect sensed data from external adversaries, while assuming that the sensing device itself is secure. Meanwhile, both the scale and frequency of IoT-focused attacks are growing. This prompts a natural question: how to protect sensed data even if all software on the device is compromised? Ideally, in order to achieve this, sensed data must be protected from its genesis, i.e., from the time when a physical analog quantity is converted into its digital counterpart and becomes accessible to software. We refer to this property as PfB: Privacy-from-Birth.In this work, we formalize PfB and design Verified Remote Sensing Authorization (VERSA) – a provably secure and formally verified architecture guaranteeing that only correct execution of expected and explicitly authorized software can access and manipulate sensing interfaces, specifically, General Purpose Input/Output (GPIO), which is the usual boundary between analog and digital worlds on IoT devices. This guarantee is obtained with minimal hardware support and holds even if all device software is compromised. VERSA ensures that malware can neither gain access to sensed data on the GPIO-mapped memory nor obtain any trace thereof. VERSA formally verified and its open-sourced implementation targets resource-constrained IoT edge devices, commonly used for sensing. Experimental results show that PfB is both achievable and affordable for such devices.

Privacy-from-Birth: Protecting Sensed Data from Malicious Sensors with VERSA

The goal of a sovereign joinoperation is to compute a query across independent database relations such that nothing beyond the join results is revealed. Each relation involved in a sovereign join is owned by a distinct entity and the party posing the query is distinct from the relation owners; it is not permitted to access the original relations. One notable recent research result proposed a secure technique for executing sovereign joins. It entails data owners sending their relations to an independent database service provider which executes a sovereign join with the aid of a tamper-resistant secure coprocessor. This achieves the goal of preventing information leakage during query execution. However, as we show in this paper, the proposed technique is actually insecure as it fails to prevent an attacker from learning the query results. We also suggest some measures to remedy the security problems.

On Security of Sovereign Joins.

This report documents the program of the Dagstuhl Seminar 12381 "Privacy-Oriented Cryptography", which took place at Schloss Dagstuhl in September 16-21, 2012. Being the first Dagstuhl seminar that explicitly aimed to combine cryptography and privacy research communities, it attracted a high number of participants, many of whom were new to Dagstuhl. In total, the seminar was attended by 39 international researchers, working in different areas of cryptography and privacy, from academia, industry, and governmental organizations. The seminar included many interactive talks on novel, so-far unpublished results, aiming at the design, analysis, and practical deployment of cryptographic mechanisms for protecting privacy of users and data. The seminar featured two panel discussions to address various approaches towards provable privacy and different challenges but also success stories for practical deployment of existing cryptographic privacy-oriented techniques.

https://drops.dagstuhl.de/opus/volltexte/2013/3755/pdf/dagrep_v002_i009_p165_s12381.pdf/

Privacy-Oriented Cryptography (Dagstuhl Seminar 12381).

Preventing abuse of web services by bots is an increasingly important problem, as abusive activities grow in both volume and variety. CAPTCHAs are the most common way for thwarting bot activities. However, they are often ineffective against bots and frustrating for humans. In addition, some recent CAPTCHA techniques diminish user privacy. Meanwhile, client-side Trusted Execution Environments (TEEs) are becoming increasingly widespread (notably, ARM TrustZone and Intel SGX), allowing establishment of trust in a small part (trust anchor or TCB) of client-side hardware. This prompts the question: can a TEE help reduce (or remove entirely) user burden of solving CAPTCHAs? 
In this paper, we design CACTI: CAPTCHA Avoidance via Client-side TEE Integration. Using client-side TEEs, CACTI allows legitimate clients to generate unforgeable rate-proofs demonstrating how frequently they have performed specific actions. These rate-proofs can be sent to web servers in lieu of solving CAPTCHAs. CACTI provides strong client privacy guarantees, since the information is only sent to the visited website and authenticated using a group signature scheme. Our evaluations show that overall latency of generating and verifying a CACTI rate-proof is less than 0.25 sec, while CACTI's bandwidth overhead is over 98% lower than that of current CAPTCHA systems.

Gene Tsudik

Papers

Proceedings of the 13th international conference on Information security

Privacy-from-Birth: Protecting Sensed Data from Malicious Sensors with VERSA

On Security of Sovereign Joins.

Privacy-Oriented Cryptography (Dagstuhl Seminar 12381).

CACTI: Captcha Avoidance via Client-side TEE Integration